Method for improving NGS sequencing accuracy and application

Abstract

The invention relates to the technical field of biology, and particularly provides a method for improving NGS sequencing accuracy and application thereof.The method comprises the steps that NGS sequencing is conducted on a sample library to obtain initial sequencing data, and the library content of the sample library is not lower than 0.5 ng; and analyzing the initial sequencing data to obtain a sample sequence. According to the method, NGS sequencing can be carried out on a sample based on sanger sequencing, and finally obtained sequencing data has remarkable advantages in the aspects of assembly success rate, redundancy rate, assembly result verifiability and the like.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a method for improving the accuracy of NGS sequencing and its application. Background Art

[0002] Sanger sequencing is also known as the first generation of DNA sequencing technology. Although Sanger sequencing can only measure a sequence of 600-1000bp in length, it is still widely used in vector construction, gene knockout and other aspects. However, when the sample insert fragment used for Sanger sequencing is long, multiple primer designs and sequencing are required, which has a high cycle and cost; when the number of samples that need Sanger sequencing is very large, it is impossible to read the complete information in a single Sanger read.

[0003] Next Generation Sequencing (NGS) refers to a high-throughput sequencing technology that can sequence hundreds of thousands to millions of nucleic acid molecules at the same time. Through NGS sequencing and subsequent assembly of sequencing reads, high-throughput data output can be achieved, which has a wide range of applications in production and research.

[0004] Compared with conventional NGS sequencing, the data accuracy obtained when using NGS sequencing to sequence Sanger sequencing samples is lower. When performing NGS sequencing, sufficient DNA input, final library quality, and number of sample labels (such as barcodes) are required to meet the requirements of high-throughput sequencers. The DNA input amount for Sanger sequencing samples is itself small, and when the number of input samples for NGS sequencing is large, the mutual contamination between samples during library preparation will increase, which in turn affects the data volume and coverage of the target sequence, resulting in incomplete or low-quality assembled sequences, making the NGS sequencing accuracy of Sanger sequencing samples low.

[0005] Therefore, it is urgent to propose a method to improve the accuracy of NGS sequencing for Sanger sequencing samples. Summary of the invention

[0006] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, an embodiment of the present invention proposes a method for improving the accuracy of NGS sequencing and an application of the method. The method proposed in the embodiment of the present invention can perform NGS sequencing on samples sequenced by Sanger sequencing, and the sequencing data finally obtained has significant advantages in terms of assembly success rate, redundancy rate, and verifiability of assembly results.

[0008] In a first aspect, an embodiment of the present invention provides a method for improving the accuracy of NGS sequencing, comprising:

[0009] Performing NGS sequencing on a sample library to obtain initial sequencing data, wherein the library content of the sample library is not less than 0.5 ng; and analyzing the initial sequencing data to obtain a sample sequence, wherein the sample library is one or at least two.

[0010] In some embodiments, the library content of the sample library is not less than 1 ng.

[0011] In some embodiments, the method further comprises: constructing a sample library based on the sample, wherein the initial input amount of the sample is no more than 10 ng. In some embodiments, the initial input amount of the sample is no more than 5 ng.

[0012] In some embodiments, the sample is a PCR product, a plasmid BAC library or a BIBAC library, a complex engineered bacterium and / or a gene synthesis product. Optionally, the PCR product and / or the gene synthesis product is 200bp-20kb in size, and the plasmid BAC library or BIBAC library or the complex engineered bacterium is less than 30kb in size.

[0013] In some embodiments, constructing a sample library based on the sample specifically includes: processing the sample using a Tn5 transposon to obtain sample fragments, wherein the Tn5 transposon includes a Tn5 transposase and a linker embedded in the Tn5 transposase, and the sample fragments are connected to the linker; and using primers connected to sample tags, PCR amplifying the sample fragments to obtain PCR amplified sequences carrying sample tags.

[0014] In some embodiments, the analyzing the initial sequencing data to obtain a sample sequence includes: filtering the initial sequencing data to remove repeated PCR amplification sequences in the initial sequencing data to obtain first intermediate data; truncating the first intermediate data to normalize the data amount to obtain second intermediate data; and assembling the second intermediate data to obtain the sample sequence.

[0015] In some embodiments, the amount of the second intermediate data is 200K-1G, in some embodiments, 300K-0.5G, in other embodiments, 500K-1M, preferably 0.75M.

[0016] In some embodiments, the assembling of the second intermediate data includes: performing a first round of assembly on the second intermediate data based on a first depth; in response to obtaining the result of the first round of assembly, using the result of the first round of assembly as the sample sequence; in response to not obtaining the result of the first round of assembly, performing a second round of assembly on the second intermediate data based on a second depth; in response to obtaining the result of the second round of assembly, using the result of the second round of assembly as the sample sequence; in response to not obtaining the result of the second round of assembly, performing a third round of assembly on the first intermediate data based on a third depth; in response to obtaining the result of the third round of assembly, using the result of the third round of assembly as the sample sequence, wherein the first depth is greater than the second depth, and the third depth is the same as or different from the second depth.

[0017] In some embodiments, the assembly is a k-mer assembly.

[0018] In some embodiments, after assembling the second intermediate data, the method further includes: removing redundant sequences in the first round assembly result, the second round assembly result, or the third round assembly result to obtain the sample sequence.

[0019] In some embodiments, the method further includes: calculating the sequence abundance of each sequence in the first round assembly result, the second round assembly result or the third round assembly result after redundancy removal, and taking the sequence with the highest sequence abundance among the sequences as the sample sequence.

[0020] In some embodiments, when there are at least two sample libraries, the method further comprises: mixing at least two sample libraries and performing NGS sequencing to obtain the initial sequencing data, wherein the library content of each of the at least two sample libraries is not less than 0.5 ng.

[0021] The method for improving the accuracy of NGS sequencing provided by the embodiment of the present invention has the following beneficial effects:

[0022] (1) Effectively improve the efficiency of NGS sequencing assembly based on Sanger sequencing samples;

[0023] (2) Solve the redundancy problem of multiple similar sequences in the sequencing results;

[0024] (3) Quickly and accurately determine the target sequence of the sample. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a method for improving the accuracy of NGS sequencing according to an embodiment of the present invention.

[0026] Figure 21 is a flow chart of a sequencing data analysis method according to an embodiment of the present invention.

[0027] Figure 3 The invention relates to a k-mer-based assembly method.

[0028] Figure 4 4 is a flow chart of a method for calculating sequence abundance according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0030] The present invention is made based on the following knowledge of the inventors:

[0031] Sanger sequencing has a very high sequencing accuracy and is still the gold standard in the field. In related technologies, when the sample starting amount is extremely low and the accuracy requirement is high, Sanger sequencing is often chosen. However, Sanger sequencing has a short read length and low throughput, and can only complete short read length sequencing of about 600-1000bp for one sample chain at a time. Therefore, Sanger sequencing is not competent for large quantities of samples or samples with longer fragments to be tested.

[0032] NGS sequencing technology is widely used for its high throughput and long read length after assembly. In the implementation of existing NGS sequencing technology, DNA templates for samples for PCR amplification can be obtained through DNA fragmentation, end repair and connector connection. Based on the template, by adding sample tags (such as barcodes) to PCR primers and performing PCR amplification based on the primers, all fragmented template DNAs can be added with the same sequence tags, and the tag sequence can be used to distinguish samples after the samples are mixed in the pool later. The template chain carrying the tag is also copied during the PCR amplification process, and the purpose of amplifying the library carrying the tag is finally achieved. Then, the read length sequence of the independent sample can be obtained by NGS sequencing and sample tag splitting. The above initial sequencing data will be assembled by assembly software to obtain the final sequencing sequence (that is, the sample sequence) in the next step.

[0033] When using NGS to sequence samples to be sequenced by Sanger, high-throughput sequencing and sequence assembly can achieve high-throughput, long-read sample sequence restoration. However, in practical applications, when performing NGS sequencing on samples to be sequenced by Sanger, there are a lot of sequence redundancy problems: for most current NGS sequencing, the machine library often needs to be amplified by PCR during the construction process. Since PCR has different amplification biases for different molecules, some molecules will appear "more" in the sequencing library, which is the main source of sequence redundancy. In addition, since the Sanger sequencing sample sequence is short, the amount of data required is not much, and the starting amount of DNA is extremely low, a large number of samples are required to meet the cost requirements when using a high-throughput sequencer. Since the number of samples may reach tens of thousands, there may be contamination between adjacent wells, and the contamination will be further increased after the later PCR reaction. There will also be deviations in the library pooling process. Therefore, in actual applications, when performing NGS sequencing on samples to be sequenced by Sanger, there is a problem of sample contamination. The sample contamination problem will lead to deviations in sequence abundance, thereby causing errors in determining the final sequence based on abundance, resulting in low accuracy of the NGS sequencing results for samples to be sequenced by Sanger.

[0034] Based on this, the inventors have proposed a method and application for improving the accuracy of NGS sequencing in the embodiments of the present invention after a large number of experiments and verifications. The inventors were surprised to find that the method for improving the accuracy of NGS sequencing in the embodiments of the present application can effectively remove redundant sequences in the sequencing data, improve the assembly efficiency, and overcome the contamination problem between samples, thereby greatly improving the accuracy of the NGS sequencing results for samples to be sequenced by Sanger sequencing.

[0035] Figure 1 This is a flow chart of a method for improving NGS sequencing accuracy proposed in an embodiment of the present invention. The method may include: steps S101-S105.

[0036] S101: Sample library construction.

[0037] In the embodiments of the present invention, "Sanger sequencing samples" refer to samples suitable for Sanger sequencing, which may be plasmids, PCR products, plasmid BAC libraries or BIBAC libraries, complex engineered bacteria, gene synthesis products, genomic DNA sequences (gDNA), etc. When these samples are sequenced using NGS, the sequencing results are not accurate enough due to the defects of NGS sequencing itself; at the same time, a large number of redundant reads will appear when these samples are sequenced by NGS, making it difficult to assemble or the sequence accuracy after assembly is low. Therefore, these samples are more suitable for Sanger sequencing. In some embodiments, the size of the PCR product and / or gene synthesis product can be 200bp-20kb. In other embodiments, the size of the plasmid or the genomic DNA sequence (gDNA) is less than 30kb. In other embodiments, the size of the plasmid BAC library or BIBAC library or complex engineered bacteria is less than 30kb.

[0038] In an embodiment of the present invention, "sample library" refers to a collection of library molecules that are processed to form an effective library that can be sequenced on a machine. In some embodiments, library construction may include: using a Tn5 transposon to process the sample to obtain a sample fragment, wherein the Tn5 transposon includes a Tn5 transposase and a connector embedded therein (i.e., a Tn5 transposase), and the sample fragment is connected to a connector; and using a primer connected to a sample tag, PCR amplification of the sample fragment to obtain a PCR amplification sequence carrying a sample tag. In some embodiments, library construction may also include: purifying the PCR amplification sequence carrying the sample tag to obtain a sample library. In other embodiments, library construction may include: using ultrasound / enzyme digestion to interrupt the sample, and based on the fragmented sample, sequentially performing end repair, connector connection, PCR amplification and other steps to obtain a sample library.

[0039] S102: Sample library quantification.

[0040] In an embodiment of the present invention, after the sample library is constructed, the sample library is quantified, and a library not less than 0.5ng is taken for subsequent sequencing. The present invention does not particularly limit the maximum value of library quantification. If the library amount of the sample after library construction is large (for example: 300ng), the corresponding input volume can be calculated according to the specific concentration so that its final input amount is not less than 0.5ng, for example, 0.5ng-10ng, 0.5ng-5ng, 0.5ng-2.5ng, 0.5ng-1ng or any value therebetween. In an embodiment of the present invention, the library quantification method includes but is not limited to qPCR based on real-time fluorescence quantification and Qubit quantitative method based on fluorescence spectrum. In an embodiment of the present invention, by quantifying the sample library and taking the library amount within a certain threshold for subsequent sequencing, the accuracy of the sequence abundance after assembly can be effectively improved to produce sequencing results with high accuracy, and it is of great significance to reduce the redundancy rate of library construction, remove sample contamination, and improve sequencing accuracy.

[0041] S103: Sequencing the sample library.

[0042] In the embodiment of the present invention, NGS sequencing is performed on the sample library to obtain initial sequencing data. In the embodiment of the present invention, "initial sequencing data" refers to the original sequencing data read by the sequencing platform after NGS sequencing is performed on the library, that is, the data off the machine, which includes the reads of the adapter sequence, the reads of the sample, etc. The present invention does not limit the sequencing platform and the specific sequencing method.

[0043] S104: Sequencing data analysis.

[0044] In an embodiment of the present invention, a sample sequence can be obtained by analyzing the initial sequencing data. In an embodiment of the present invention, data analysis refers to the process of processing the initial sequencing data produced by the sequencing platform using bioinformatics-based software, command lines, scripts, etc. to obtain various intermediate data files, and determining the final sequencing results, i.e., the sample sequence, based on the various intermediate data files. According to the method proposed in any embodiment of the present invention, an accuracy rate of 80%-100% for samples (sample sequences to be tested / sample sequences) based on Sanger sequencing can be achieved.

[0045] Figure 2 Schematic diagram of the sequencing data analysis method according to an embodiment of the present invention. Figure 2 As shown, the method may include: steps S201-S203.

[0046] Step S201: Filter the initial sequencing data to obtain first intermediate data.

[0047] In some embodiments, by filtering the initial sequencing data, repeated PCR amplification sequences generated by PCR amplification during library construction and / or sequencing can be removed from the initial sequencing data, and the first intermediate data (i.e., the data retained after the initial sequencing data is filtered and the repeated PCR amplification sequences are removed) can be obtained. Furthermore, this filtering step can also include quality control of reads, that is, through this filtering step, the adapter sequences, sequences with too low reads, and sequences with GC content and distribution that do not meet the species standards in the initial sequencing data are removed to improve the data quality. In some embodiments, SOAPnuke (https: / / github.com / BGI-flexlab / SOAPnuke), AfterQC (https: / / github.com / OpenGene / AfterQC), Fastp (https: / / github.com / OpenGene / fastp), Fastqc (http: / / www.bioinformatics.babraham.ac.uk / projects / fastqc), seqtk (https: / / github.com / lh3 / seqtk), trimmomatic (http: / / wwwusadellab.org / cms / ?page=trimmomatic) and other software and / or self-written scripts / command lines can be used to filter the initial sequencing data, and the present invention is not limited to this. It is understood that by filtering the initial sequencing data, the sequencing accuracy can be effectively improved in subsequent analysis.

[0048] Step S202: intercepting the first intermediate data to obtain second intermediate data.

[0049] In the embodiment of the present invention, the first intermediate data is intercepted to normalize the data volume to obtain the second intermediate data (i.e., the intermediate data including the normalized data). In some embodiments, the data volume of the second intermediate data after the normalization data volume may be between 200k and 1G. In other embodiments, the data volume of the second intermediate data after the normalization data volume may be between 300K and 0.5G or 500K and 1M. In other embodiments, the data volume of the second intermediate data after the normalization data volume may be 0.75M. In some embodiments, seqtk( https: / / github.com / lh3 / seqtk ) and / or self-written scripts / command lines, etc. to intercept the data. It can be understood that, in the embodiment of the present invention, intercepting through the first intermediate data can make the data amount uniform, thereby generating a relatively stable computing resource to be assembled.

[0050] Step 203: Assemble the second intermediate data to obtain a sample sequence.

[0051] In the embodiment of the present invention, the sample sequence can be obtained by assembling the first intermediate data and / or the second intermediate data. In the embodiment of the present invention, due to the limitation of the read length of the sequencing technology, in the sequencing of the genome / metagenome, the original DNA of the sample needs to be broken into small fragments for sequencing, and based on the sequencing data, these short sequences (reads) are used to construct the genome map / metagenome sequence, which is the "assembly" process.

[0052] The sequence assembly in the embodiment of the present invention can be specifically based on contig, scaffold, N50 or k-mer assembly. In the embodiment of the present invention, k-mer refers to dividing a sequence into substrings containing k bases, and mer represents a monomer unit, which is equivalent to nt or bp. If the read length is L and the k-mer length is set to k, the number of k-mers generated is: L-k+1. For example, for the sequence AACTGACT, if k is set to 3, it can be divided into AAC ACT CTG TGAGACACT, a total of 6 k-mers. Among them, k must be an odd number. If it is an even number, encountering a palindrome sequence may generate exactly the same k-mers. In the embodiment of the present invention, the k-mer depth refers to the frequency of occurrence of each k-mer, and can also be referred to as the k-mer abundance. In one embodiment of the present invention, the assembly can be a k-mer-based assembly.

[0053] Figure 3 A k-mer-based assembly method according to an embodiment of the present invention is shown. Figure 3As shown, the method includes: firstly, performing a first round of assembly based on assembly condition 1, that is, performing a first round of assembly on the second intermediate data based on the first depth. After the first round of assembly is completed, checking whether the first round of assembly results exist. In response to obtaining the first round of assembly results, the first round of assembly results are used as a sample sequence; in response to not obtaining the first round of assembly results, that is, the first round of assembly fails, performing a second round of assembly based on assembly condition 2, that is, performing a second round of assembly on the second intermediate data based on the second depth. After the second round of assembly is completed, checking whether the second round of assembly results exist. In response to obtaining the second round of assembly results, the second round of assembly results are used as the sample sequence; in response to not obtaining the second round of assembly results, that is, the second round of assembly fails, performing a third round of assembly based on assembly condition 3, that is, performing a third round of assembly on the first intermediate data based on the third depth. After the third round of assembly is completed, checking whether the third round of assembly results exist. In response to obtaining the third round of assembly results, the third round of assembly results are used as a sample sequence. In some embodiments, the first depth is greater than the second depth, and the third depth can be the same as or different from the second depth. In the embodiment of the present invention, the first depth, the second depth, and the third depth can be determined according to the specific needs of the user and the actual situation of the data volume. Specifically, the first depth may be 3-10 or any value therebetween; the second depth may be 10-100 or any value therebetween; and the third depth may be 100-1000 or any value therebetween. In an embodiment of the present invention, the assembly process may use software such as SOAPdenovo2 (GitHub-aquaskyline / SOAPdenovo2:Next generationsequencing reads de novo assembler), trimmomatic (http: / / www.usadellab.org / cms / ?page=trimmomatic), Trinnity (https: / / github.com / trinityrnaseq / trinityrnaseq) and / or self-written scripts / command lines.

[0054] It is understandable that through multiple rounds of assembly under different k-mer assembly conditions, the success rate and accuracy of assembly can be greatly improved. At the same time, multiple rounds of assembly based on library quantification, filtering, and data interception can effectively solve the redundancy problem of multiple similar sequences in the sequencing results. At the same time, the similarity between different sample sequences is extremely low, thereby avoiding contamination problems and showing a higher assembly advantage.

[0055] The method for improving the accuracy of NGS sequencing proposed in the embodiment of the present invention may further include step S105: determining the sample sequence. It is understandable that after the sequencing data analysis based on step S104, the sample sequence can be determined based on the assembled sequence. In some embodiments, Figure 4 As shown, step S105 may include: S401-S402.

[0056] S401: Remove redundant sequences in the assembly results.

[0057] In the embodiment of the present invention, software such as redundans (https: / / github.com / Gabaldonlab / redundans) and cd-hit (http: / / weizhong-lab.ucsd.edu / cd-hit / ) and / or self-written scripts / command lines can be used to remove redundant sequences in the assembly results (i.e., the first round assembly results, the second round assembly results, or the third round assembly results). In the embodiment of the present invention, the redundant sequences in the assembly results are removed, which effectively solves the similarity redundancy problem between samples, thereby greatly reducing the similarity between samples to solve the possible contamination problem between samples, and effectively improves the accuracy of the assembly results.

[0058] S402: Calculate the sequence abundance of each sequence in the assembly result after redundancy removal, and determine the sequence with the highest sequence abundance among the sequences as the sample sequence.

[0059] In an embodiment of the present invention, the sequence abundance of each sequence in the assembly result after de-redundancy (i.e., the first round assembly result, the second round assembly result, or the third round assembly result after de-redundancy) can also be calculated, and the sequence with the highest sequence abundance among the sequences can be used as the sample sequence. In an embodiment of the present invention, the REM module in the Trinity assembly kit can be used to evaluate sequence abundance. It can be understood that by calculating the abundance of the assembled sequence and using the sequence with the highest sequence abundance as the sample sequence, the sample sequence can be determined quickly and accurately, and the sample with the highest abundance that is not the target sequence can be quickly located, which greatly improves the accuracy of data delivery.

[0060] In an embodiment of the present invention, when there are multiple (at least two) sample libraries, a method for improving the accuracy of NGS sequencing is also proposed, including: mixing at least two sample libraries and performing NGS sequencing to obtain initial sequencing data, wherein the library content of each sample library in the at least two sample libraries is not less than 0.5 ng; and analyzing the initial sequencing data to obtain each sample sequence of the multiple samples.

[0061] It is understandable that by performing library quantification on multiple sample libraries separately, the deviation in the library pool can be effectively corrected, and the contamination between different sample libraries can be reduced, which is beneficial to the abundance assessment and sample sequence determination in the later assembly sequence. It should be noted that the processing flow of multiple sample libraries in the embodiment of the present invention and the beneficial technical effects produced therefrom are the same as the methods and effects described in any of the above embodiments, and will not be repeated here.

[0062] The method for improving the accuracy of NGS sequencing proposed in the embodiment of the present invention can be applied in gene cloning, gene editing, species identification and / or molecular breeding. Specifically, the method proposed in the embodiment of the present invention can be applied to high-throughput sequencing of plasmids, PCR products, plasmid BAC libraries or BIBAC libraries, complex engineered bacteria, gene synthesis products, etc.

[0063] The experimental methods in the following examples, unless otherwise specified, are all conventional methods, and are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can all be obtained from commercial channels.

[0064] Unless otherwise specified, the quantitative tests in the following examples were performed three times and the results were averaged.

[0065] Example 1

[0066] In this example, sample a (SEQ ID NO: 1, the specific sequence is as follows) was sequenced by Sanger sequencing and NGS sequencing methods, respectively. The library construction, sequencing and data analysis in NGS sequencing implemented the existing process, and specifically, reference can be made to MGIEasy Universal DNA Library Preparation Reagent Set (MGI, Catalog Number: 1000006985), MGISEQ-2000RS High-Throughput Sequencing Reagent Set (MGI, Catalog Number: 1000012551) and conventional RNA data filtering (software website https: / / github.com / BGI-flexlab / SOAPnuke) and conventional RNA data assembly (https: / / github.com / trinityrnaseq / trinityrnaseq) processes; the reagents used for Sanger sequencing were the Applied Biosystems Sanger sequencing kit provided by Thermo Fisher Scientific, and the specific experimental process can be referred to https: / / www.thermofisher.cn / cn / zh / home / life-science / sequencing / sanger-sequencing / sanger-sequencing-workflow.html.html. After sequencing, the sequence obtained by the NGS sequencing method using the existing assembly process is compared with the sequence obtained by Sanger sequencing. The specific steps are as follows.

[0067] Sample a sequence (SEQ ID NO: 1):

[0068] GACGGCATGTCTAGTATAAGCAATTATACCGCGAAACTGCGAATGGCTCATTAAATCAGTTATCGTTTATTTGATAGTACCTTACTACTTGGATAACCGTGGTAATTCTAGAGCTAATACATGCTAAAAACCCCAACTCACGAAGGGGTGTATTTATTAGATTAAAAACCAATGCCCTTCGGGGCTTCTTGGTGATTCATAATAACTTCTCGAATCGCATGGCCTTGCGCCGGCGATGGTTCATTCAAATTTCTGCCCTATCAACTTTCGATGGCAGGGTCTTGGCCTGCCATGGTTACAACGGGTAACGGAGGGTTAGGGCTCGACCCCGGAGAAGGAGCCTGAGAAACGGCTACTACATCCAAGGAAGGCAGCAGGCGCGCAAATTACCCAATCCCGACTCGGGGAGGTAGTGACAATAAATACTGATACAGGGCTCTTTTGGGTCTTGTAATTGGAATGAGTAC

[0069] AATTTAAATCCCTTAACGAGGAACAATTGGAGGGCAAGTCTGGTGCCAGCAGCCGCG

[0070] GTAATTCCAGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAAAGCTCGTAGTTGA

[0071] ACCTTGGGCCTGGCTGGCCGGTCCGCCTCACCGCGTGCACTGGTTCGGCCGGGCCTT

[0072] TCCCTCTGGGGAACCGCATGCCCTTCACTGGGTGTGTCGGGGAACCAGGACTTTTACT

[0073] GTGAAAAAATTAGAGTGTTCAAAGCAGGCCTATGCTCGAATACATTAGCATGGAATAA

[0074] TAGAATAGGACGTGTGGTTCTATTTTGTTGGTTTCTAGGACCGCCGTAATGATTAATAG

[0075] GGACAGTCGGGGGCGTCAGTATTCCGTTGTCAGAGGTGAAATTTCTGGATTTACGGAG

[0076] ACTACTACTGCGAAAGCATTCGCCAGGATGTTTTCATTATCAGACGAAGTAGGGATCG

[0077] AGACGATCAGATACCGTCGTAGTCTACATAACTATGCGACTAGGGATCGGCGATGTTAT

[0078] TATTTGACTCGCTCGGACCTTACGAGAATCAAAGTCTTTGGGTTCTGGGGGGAGTATG

[0079] TCCAGTGTG

[0080] (1) Sample A was sequenced using Sanger sequencing and the measured length was 1057 bp.

[0081] (2) Sequence sample a using the NGS sequencing method, and implement the existing assembly process to obtain the sample assembly results.

[0082] (3) The NGS sequencing assembly results of sample a were aligned with the Sanger sequencing results using blasr (Chaisson MJ, Tesler G. Mapping single molecule sequencing reads using basic local alignment with successive refinement (BLASR): application and theory [J]. BMC bioinformatics, 2012, 13 (1): 238). The alignment results are shown in Table 1.

[0083] Table 1 Comparison of NGS sequencing assembly results and Sanger sequencing results of sample a

[0084]

[0085] According to Table 1 of this embodiment, a total of 3 sequences were obtained for the NGS sequencing assembly results of sample a, and the lengths of TR4|c0_g2_i1 and TR4|c0_g1_i1 in the 3 sequences were both 1125bp, and the similarity with the Sanger sequence alignment was high, both greater than 97%. According to the start and end positions of the alignment results, the sequences of the two assembly sequences are similar and have the same length, and can be determined as redundant sequences. This result shows that the NGS sequencing method that implements the existing assembly process has redundancy problems when applied to samples based on Sanger sequencing.

[0086] Example 2

[0087] In this example, samples b and c (SEQ ID NO: 2 and SEQ ID NO: 3, the specific sequences are as follows) were sequenced by Sanger sequencing and NGS sequencing methods, respectively, wherein library construction, NGS sequencing and data analysis were the same as in Example 1.

[0088] Sample b sequence (SEQ ID NO: 2):

[0089] CCTAGCTTGAACTGGCTCATCTGTTAAGAACAACTTTGGTGGCAATGCAAATCTTGCC

[0090] ACTGATGGCACAATTACATTTACAAATATTGGCGGCACAGGGCAAGATACAATCCACG

[0091] ATGCGATTAATAATGTTCTCACCAAATTGATCTCGCTTTCGGCAACAGAAGAAGTGGT

[0092] GTCAGGGGAAGCTGTCTATGAAGCACTTAAAAGTGCAAAACCAACAACGGTTTCAGC

[0093] AGAAGCCAACAAAGGCATTAAAGTTACTGGCGAGAAATCATCAAACACCACAGGCAA

[0094] CAGCTTCACAATTGGCTTAGACGATGCCACCTTGAACAAAATCAACAATGCCGTGAAT

[0095] CAAGATCTTTCAAACCTCGGTGAAGATGGCAAAAATGCCATTACTGGCTTGGTGGATG

[0096] TGGTGAAAAAAGCAAATTCACCGATCACAGTTGAGTCTTCTACCGATAACAACAAGA

[0097] AAAAAACCTTCACTGTCGGCTTAGAGAAAAACATTACAGAGGTAAACAGCATTACCT

[0098] TTGATAAGTCAGGGCAAGATCCAAATCAAGTTACGGGCAGAATGAGCAGTGCGGGTT

[0099] TAACCTTCAAAAAAGGCGACACAACAAATGGTTCAACCACCACTTTTGCAGAAGATG

[0100] GCTTAACCATTGATAGCACAACAAATTCTGCTCAAACAAACTTAGTGAAAGTAAGTCG

[0101] TGATGGCTTCTCGGTGAAAAATGGCAGCGATGAAAGCAAATTAGCCCCGACAAAATTA

[0102] TCTATCGGTGCGGAAAATGCAGAACACGTTGAAGTAACTAAATCGGGCATAGCCTTAA

[0103] AAGCGAATAACACAACAGGTAAATCAAGCATTACGTTATCGGACAGTGCGATTACCCT

[0104] TGCTGCGGCTACAGCTGGAAATGCGATTAAACTCACTGGCGTTGCAGATGGCAGCATT

[0105] ACAGCAGGTTCAAAAGATGCGGTAAATGGGGGGCAGTTGCGTACCTTATAGGGGTTG

[0106] ATAGCCGGGCTAAATGGCGCTACTGAGAAAACAACGATCAGTGAGCCATTTCTGATGT

[0107] GAGCAGGCTCTACCGATGCCAAATGGCAATAAAGCGGACAATAAACGTAAACAGTTA

[0108] ATTGACTGACGATGAATTTACTAGCCGACAATATGATGCTCAATGAGATCTGTGGTGTG

[0109] AGTCTCATGCACGCACATCTCACGGAGACTACAATATTGTCCTGCCTGATTAAAATA Sample c sequence (SEQ ID NO: 3)

[0110] CTTATATAATTTTTATGTGGAATGTCATTAGCAATTTTGCTGAGTATAGCGCTAAATCCAT

[0111] AGATGGGTTTTTGATGCAACTTAGACAATATTATGTTGATTCATGAACTTGGGAGTGTT

[0112] TCTTCAACCATTTTATACCAGATCAACCCCTCTCCCAATATACGTTAGAATTGAAAAAA

[0113] ATATAGATTTCCTGGAGAGACTGGATGCCTGATCTACCTATAGTTGTGGCTCCAACTTT

[0114] CTCTCTGTTCACAGAGCCAGTAGTGTCATTGAACATCCCTTCCTTTAAAAAAACAAAC

[0115] CATTTTTCATGATTTATTTTGATCATTTTTCCTTCCTCCAACTCTTCCCCACTCATCCAAA

[0116] TCCATATTCATTTTCTTAAAAACAAAACGAAAACTGCGCTGTTTTGTCTGGATGGTGTT

[0117] TTCTTGGAGTCATTTACTGCAGTCTTTCTGACTCCTTATCTTCATAGATGCCTGAGCATT

[0118] GAAAGAAAGGGCTTGATGAAGACATCTCATCTAGGACCAAGTGTTCTAAAGTCTCTTA

[0119] CTCTCTGCACATTGGAAGACCATTTCTTAACTCCCATTTCTGTTTCTTGCTGCTTTGTAGTGGGTCCTTGTTTCTTGACCCATTATCATCATCATCTTGTTCTTCCTTCTCTCCTCTTTGAATTTGCATGGAGAATTCCTCAAGATTAC TATTTTACCCCAGGTGCATTTTTAATCTTAATGTTAAGAATATGCAATGAGGCAGGCAGTGGTGGCACACACCTTTAATCCCAGCACTTGGGAGGTGGAGGCAGGTGGATTTCTAACTTCGAGGCCAGCCTGGTCTACAGAGTGAGTTCCA GGACATCTGGGGCTATACAGAGAAAACCTGCCTTGAAAAACAAAACAAACAAACAAACAAAGAATATGCAATAAATTACCTAGTTAGTGTACAGGAATGGAGGTGTTTTTTAAGAAAAAATATTTATTTAATGTGAGTACACTGTCGGGTC ACTGTTCAGAATACATCAGAAAGAGGTTAGATATCGGGATCTCACCTTACGGATGGATTGGTGAGACCACCTACCGAGTTGTTGAGAATTGAACTCAAGGCCTCTGGAAGACGAGGCAGATCTTCTAACCACTGGACTTACTTCTCCAGAC

[0120] Sanger sequencing was performed on samples b and c respectively. The length of sample b was measured by Sanger sequencing to be 1210 bp. At the same time, after NGS sequencing and data analysis, the assembly result of sample b included 4 sequences. The abundance of these 4 sequences was ranked using REM in Trinity's assembly kit, and the results are shown in Table 2.

[0121] Table 2 Sequence abundance statistics in NGS sequencing assembly results of sample b

[0122] Sequence ID length expected_count percentage TR2|c0_g1_i1 2677 7668 61.40％ TR1|c0_g1_i1 1203 4163 33.30％ TR3|c0_g1_i1 477 367 2.90％ TR4|c0_g1_i1 452 294 2.40％

[0123] According to Table 2, among the four sequences obtained by the NGS sequencing assembly results of sample b, the sequence with the highest abundance is TR2|c0_g1_i1, which is 61.40%; followed by TR1|c0_g1_i1, which ranks second in abundance, at 33.30%.

[0124] The two sequences ranked first and second in abundance of sample B were compared with the Sanger sequencing results of sample C using blasr. The comparison results are shown in Table 3.

[0125] Table 3 Comparison of NGS sequencing assembly results of sample b and Sanger sequencing results of sample c

[0126]

[0127] As shown in Table 3, when the NGS sequencing of sample b was assembled, the sequence TR2|c0_g1_i1 with the highest abundance was compared with that of sample c. When the Sanger sequencing results of sample a are compared, the sequence can be successfully compared with the Sanger sequencing results of sample c. The above results show that when the NGS sequencing method implementing the existing assembly process is applied to samples based on Sanger sequencing, there is a problem of mutual contamination between samples, resulting in the abundance of the target sequence being lower than that of the non-target sequence.

[0128] Example 3 Preparation and sequencing of the first batch of sample libraries

[0129] In this example, library construction and NGS sequencing were performed on 875 Sanger sequencing samples.

[0130] 3.1 Preparation of sample library

[0131] The 875 samples sequenced by Sanger were PCR samples and plasmid samples, of which the size of PCR samples was 200bp-20kb and the size of plasmid samples was less than 30kb.

[0132] (1) Prepare Tn5 transposon. Vortex 20 μl of Tn5 transposase (1 U / μl, Catalog No.: BGE005) and 5 μl of Annealed adapter (50 uM, Catalog No.: BGE005) and let stand at room temperature (25°C) for 1 hour. After standing, the Tn5 transposome solution (Transposome) was stored at -20°C.

[0133] (2) Sample DNA fragmentation. Use an automated platform to prepare a 5ul reaction system for 875 samples. The system includes: 1μl 5x TAG buffer (Cat. No.: BGE005), 5ng DNA per sample, 0.2ul Tn5 transposon solution, and NF water. Mix the reaction system thoroughly on ice. Place the reaction system at 55°C for 10 minutes. After the reaction is completed, add 1.25ul 5x NTBuffer (Cat. No.: BGE005) and mix thoroughly, then place at room temperature for 5 minutes. After the reaction is completed, terminate the reaction to obtain a DNA template. The DNA template is the sample DNA that has been fragmented, end-repaired, and connected to adapters.

[0134] (3) PCR amplification and sample label (barcode) labeling are performed on the DNA template simultaneously. The above-mentioned simultaneous PCR amplification and barcode labeling are achieved by adding a barcode label to the PCR primer. In this embodiment, the barcode label is an MGI double barcode. 6.5ul DNA template, 5ul 5×KAPA Hifidelity buffer (KK2102), 0.75ul dNTP (10mM each, KK2102), 0.5ul KAPA Hifi Polymerase (1U / ul, KK2102), 0.75ul PCR index R (10uM, catalog number: 1000022805), 0.75ul PCR index L (10uM, catalog number: 1000022805), and 11ul NF water are mixed on ice to form a 25ul reaction system. Based on the above reaction system, PCR reaction was carried out with the following reaction conditions: 72°C for 3 min, 95°C for 3 min, 98°C for 20 s, 60°C for 30 s, 72°C for 20 s, 72°C for 5 min, 4°C∞.

[0135] (4) Purification of PCR products to obtain sample libraries. After the PCR product library of a single sample is purified, it is mixed evenly according to a certain volume, and 200ul of the mixed library is purified according to the following steps. The specific purification steps include:

[0136] Place Ampure XP Beads (Cat. No.: 1000000144A63881) at room temperature 30 min in advance and shake thoroughly before use;

[0137] Add 1 volume of magnetic beads to the centrifuge tube with the mixed PCR product, and use a pipette to gently pipette the liquid in the tube at least 10 times, and then incubate at room temperature for 5 minutes;

[0138] Place the centrifuge tube with magnetic beads on a magnetic stand, let it stand for 2-5 minutes until the liquid becomes clear, and then discard the supernatant;

[0139] Keep the centrifuge tube with magnetic beads on the magnetic rack, add 500 μl of freshly prepared 80% ethanol, let it stand at room temperature for 30 seconds, and then discard the supernatant;

[0140] Repeat the previous step once and try to dry the liquid at the bottom of the tube;

[0141] Keep the centrifuge tube with magnetic beads on the magnetic rack, open the tube cover, and let it dry at room temperature until the surface of the magnetic beads is not reflective or cracked;

[0142] Remove the centrifuge tube with magnetic beads from the magnetic stand and add 42 μl TE. Use a pipette to mix the liquid in the tube and place it at room temperature for 5 minutes to elute the DNA.

[0143] Place the centrifuge tube on a magnetic rack for 2-5 minutes until the liquid becomes clear, and then transfer the supernatant to a new centrifuge tube to obtain the purified PCR product, indicating that the library preparation is complete.

[0144] 3.2 Quantification of sample library

[0145] The Qubit 1X dsDNA HS Assay Kit (Thermo Fisher, Catalog No.: Q33230) was used and the following procedures were performed according to its instructions.

[0146] (1) Select an appropriate pipette according to the library volume and read the library volume, which is recorded as V.

[0147] (2) Determine the number of samples to be tested and add two additional standards from the kit; prepare a corresponding number of 0.5 ml PCR tubes as sample tubes and standard tubes.

[0148] (3) Dilute the Qubit dsDNA fluorescent dye and Qubit dsDNA buffer in the kit at a ratio of 1:200 to prepare the Qubit working solution.

[0149] (4) Add 190ul Qubit working solution and 10ul standard into the standard tube, mix well and centrifuge.

[0150] (5) Add 199ul Qubit working solution and 1ul sample into the sample tube, mix well and centrifuge.

[0151] (6) After centrifugation, place the standard tube and sample tube in a dark place for 2 min.

[0152] (7) The concentration was measured using a Qubit 2.0 fluorometer (Thermo Fisher), and the concentration was recorded as C.

[0153] (8) The total library volume is calculated as N = V*C.

[0154] After testing, 381 samples in the first batch (875 samples) had a total library amount greater than 0.5ng, 505 samples had a total library amount greater than 0.4ng, and 594 samples had a total library amount greater than 0.3ng. It should be noted that the total library amount of most samples in this embodiment is above 0.5ng, but the total library amount of a small number of samples is less than 0.5ng. This is because the automatic sampler used by the applicant has a deviation (the sampler has a deviation during multiple pipetting processes), resulting in the total library amount of some samples being less than 0.5ng. This embodiment selects three library quantification standards of 0.5ng, 0.4ng, and 0.3ng for subsequent comparative analysis.

[0155] 3.3 NGS sequencing

[0156] The first batch of sample libraries were subjected to double-end sequencing (PE50+10+10) using an MGI2000 sequencer (MGI) to obtain initial sequencing data. According to the initial sequencing results, the first batch, i.e., 875 samples, produced 410,805,351M of data. At the same time, both batches of samples were subjected to Sanger sequencing (the same Sanger sequencing method used in Example 1) to verify the accuracy of the NGS assembly results.

[0157] Example 4 Analyze the initial sequencing data of the first batch of samples measured in Example 3

[0158] This example analyzes the initial sequencing data of the first batch of samples measured in Example 3 and obtains the sample sequences.

[0159] The following describes the analysis process and specific commands for the initial sequencing data.

[0160] (1) Split and filter. Create an index that does not allow mismatches based on the barcode sequence, and split the sample corresponding to each barcode. Filter the split sample data. This example uses SOAPnuke-1.5.6 version for filtering (https: / / github.com / BGI-flexlab / SOAPnuke / releases).

[0161] Table 4 Database construction data information of the first batch of samples

[0162] First batch (875 samples) Number of reads of offline data 410,805,351 Number of read pairs after splitting and filtering 89,558,480 The amount of data after splitting and filtering is sufficient for the number of samples 774

[0163] The sequence information after splitting and filtering is shown in Table 4, and 875 samples produced 89,558,480M. It should be noted that the splitting and filtering shown in Table 4 can evaluate whether the sample data volume is sufficient. Specifically, the length of Sanger sequencing samples generally ranges from 500kb to 30kb, and the general depth that meets the assembly is above 60X, so it is considered that the minimum data volume requirement is 500bp*60=30000bp. Therefore, samples with a data volume greater than 30000bp after data filtering are samples with sufficient data volume after splitting and filtering.

[0164] (2) Data capture. In this example, seqtk (https: / / github.com / lh3 / seqtk) software was used to capture the data. After data capture, 0.75M clean data per sample was obtained.

[0165] (3) Perform multiple rounds of assembly. In this embodiment, Trinity software is used to perform multiple rounds of assembly.

[0166] Multiple rounds of assembly process such as Figure 3 As shown, multiple rounds of different kmer depths were used for assembly. First, the sequences were assembled according to assembly condition 1, specifically using the Trinity assembly parameter --min_kmer_cov=5 for 0.75M clean data, and then confirming whether the assembly result exists. Based on the absence of the assembly result, assembly was performed according to assembly condition 2, specifically using the Trinity assembly parameter --min_kmer_cov=3 for 0.75M clean data, and then confirming whether the assembly result exists. Based on the absence of the assembly result, assembly was performed according to assembly condition 3, specifically retrieving all clean data before the sample was intercepted, and using the Trinity assembly parameter --min_kmer_cov=3 for assembly. The assembly success rates are shown in Tables 5, 6, and 8.

[0167] (4) De-redundancy is performed on the assembled sequence. In this embodiment, the redundancy removal step is performed using the redundans software.

[0168] (5) After removing redundancy, the sample sequences were obtained. The similarity of the assembly results after removing redundancy is shown in Tables 5 and 8.

[0169] (6) Calculation of sequence abundance

[0170] For the obtained two batches of sample sequences, sequence abundance calculation was performed. In this embodiment, the REM module in the Trinity assembly kit was used to perform abundance calculation, and the sample sequence was determined according to the abundance result (ie, the sequence with the largest sequence abundance). The abundance result is shown in Table 6.

[0171] (7) Result verification

[0172] The sample sequences obtained by NGS sequencing of the first batch of samples based on steps (1)-(6) of this embodiment were compared with their Sanger sequencing results (Sanger sequencing method was used in Example 1) using blasr. The specific verifiability results are shown in Tables 5, 6 and 8.

[0173] Example 5 Analysis of the initial sequencing data of the first batch of samples measured in Example 3

[0174] This embodiment uses the existing sequencing data analysis process to analyze the initial sequencing data of the first batch of samples measured in Example 3, and obtains the sample sequences.

[0175] Compared with the method used in Example 4, the method of this embodiment is different in that: 1) the method of this embodiment does not include the data interception of step (2) in Example 4; 2) the assembly process performed in the method of this embodiment is conventional assembly, specifically referring to the standard process of Trinity software; 3) the method of this embodiment does not include the redundancy removal step of step (4) in Example 4. The remaining steps are the same as in Example 4. The various indicators after assembly of the first batch of samples are shown in Table 5. Table 5 Comparison of analysis results of sequencing data of the first batch of samples

[0176]

[0177] According to the "Assembly Results" in Table 5, it can be seen from the first batch data that all samples in the first batch can be assembled into samples after multiple rounds of assembly in Example 4, while the success rate of the existing assembly process (Example 5) is only 48.06%. This shows that the assembly success rate of the NGS analysis process including multiple rounds of assembly proposed in Example 4 of the present invention is higher than the success rate of the existing assembly process.

[0178] According to the "Similarity of Assembly Results" in Table 5, it can be seen from the first batch data that the similarity of the assembly results of the first batch samples by the method proposed in Example 4 is 0, and the similarity of the assembly results by implementing the existing assembly process (Example 5) is 74.19%. "Similarity of Assembly Results" means that there are multiple assembly results, and the multiple sequences are similar to the target sequence. Therefore, the higher the similarity of the assembly results, the higher the redundancy rate. Therefore, the method proposed in Example 4 of the present invention has obvious advantages in removing sequence redundancy.

[0179] According to the "assembly result verifiability" in Table 5, it can be seen from the first batch data that the assembly result verifiability of the method proposed in Example 4 of the present invention is 83.07%, and the similarity of the assembly result of implementing the existing assembly process (Example 5) is 39.53%. The assembly result verifiability means that the assembly result is compared with the Sanger sequencing result of the sample. If the blast comparison can be used and the similarity is greater than 80%, it means that the assembly result is verifiable. Therefore, the method proposed in Example 4 of the present invention also has certain advantages in the verifiability of the assembly result, that is, the assembled sample sequence has a higher accuracy.

[0180] In summary, the method proposed in Example 4 of the present invention is a method for analyzing NGS sequencing data for sanger sequenced samples, which has significant advantages in terms of assembly success rate, redundancy rate, and verifiability of assembly results, that is, it effectively improves the NGS sequencing assembly efficiency of sanger sequenced samples and the accuracy of the assembled sequence, solves the redundancy problem of multiple similar sequences in the sequencing results, and effectively improves the efficiency and accuracy of sequencing data analysis. In particular, according to the method of Example 4 of the present application, the accuracy of NGS sequencing has been effectively improved, achieving an accuracy rate comparable to that of sanger sequencing, and is therefore conducive to high-throughput and high-accuracy sequencing of large quantities of samples.

[0181] Example 6 Correlation between library quantification and sequence abundance of the first batch of samples

[0182] The assembly success rate, verifiability of assembly results and abundance results of 381 samples with a total library amount greater than 0.5 ng, 505 samples with a total library amount greater than 0.4 ng, and 594 samples with a total library amount greater than 0.3 ng in the first batch of samples (875 samples) are shown in Table 6.

[0183] According to Table 6, among the 381 samples with a total library amount greater than 0.5ng, the number of samples whose sequence ranked first in abundance is the target sequence is 365. Therefore, when the library quantification of 0.5ng is used as the library quantification quality control standard, the accuracy of directly determining the sequence with the highest abundance as the sample sequence is 365 / 381=95.80%. Among the 505 samples with a total library amount greater than 0.4ng, the number of samples whose sequence ranked first in abundance is the target sequence is 390. Therefore, when the library quantification of 0.4ng is used as the library quantification quality control standard, the accuracy of directly determining the sequence with the highest abundance as the sample sequence is 390 / 505=77.22%. Among the 594 samples with a total library amount greater than 0.3ng, the number of samples whose sequence ranked first in abundance is the target sequence is 409. Therefore, when the library quantification 0.4ng is used as the library quantification quality control standard, the accuracy of directly determining the sequence with the highest abundance as the sample sequence is 409 / 594=68.86%. It can be seen that compared with other library contents, the sequencing results of the NGS sequencing method proposed in the present invention that includes a specific library threshold quality control (i.e., greater than 0.5ng) have a high accuracy rate, can quickly and accurately determine the sample sequence and deliver it directly, and effectively avoid delivering the wrong sample.

[0184] Table 6 Correlation between library quantification and sequence abundance

[0185]

[0186] Example 7 Preparation and sequencing of the second batch of sample libraries

[0187] The preparation method and sequencing method of the second batch of sample libraries in this embodiment are the same as those in Example 3, and the only differences from Example 3 are: 1) the number of samples in the second batch is 216, and the 216 samples are also PCR samples with a size of 200bp-20kb; 2) step (2) is manual loading (thereby the sample loading is uniform, and there is no problem of uneven pipetting volume in the automated platform).

[0188] It should be noted that, as shown in Table 6, when the total amount of the first batch of libraries in Example 3 contained samples less than 0.5 ng, the accuracy of the sequencing results was low. Therefore, this example further verified the sequencing performance using the second batch of samples (a total of 216) based on the 0.5 ng library content threshold that has been verified to have high sequencing accuracy. Based on uniform manual loading, the quantitative amounts of the 216 sample libraries in this example were all greater than 0.5 ng.

[0189] In this embodiment, 216 samples generated 476,733,401M of offline data.

[0190] Example 8 Analyze the initial sequencing data of the second batch of samples measured in Example 7

[0191] The method for analyzing the initial sequencing data of the second batch of samples in this embodiment is the same as that in Example 4.

[0192] Table 7 Database construction data information of the second batch of samples

[0193] 216 samples Number of reads of offline data 476,733,401 Number of read pairs after splitting and filtering 128,673,508 The amount of data after splitting and filtering is sufficient for the number of samples 216

[0194] After splitting and filtering the offline data, 216 samples in this embodiment produced 128,673,508M of data.

[0195] The assembly success rate, assembly result similarity, and verifiability of the second batch of this embodiment are shown in Table 8.

[0196] Example 9 Analyze the initial sequencing data of the second batch of samples measured in Example 7

[0197] The method for analyzing the initial sequencing data of the second batch of samples in this embodiment is the same as that in Example 5.

[0198] The assembly success rate, assembly result similarity, and verifiability of the second batch of this embodiment are shown in Table 8.

[0199] Table 8 Comparison of analysis results of the second batch of sample sequencing data

[0200]

[0201] According to the "Assembly Results" in Table 8, it can be seen from the data of the second batch of samples that all the second batch of samples can be assembled into samples after multiple rounds of assembly in Example 8, and the assembly success rate is 100%, while the assembly success rate is only 97.22% when the existing assembly process (Example 9) is used for assembly. This shows that the assembly success rate of the NGS analysis process including multiple rounds of assembly proposed in Example 8 of the present invention is higher than the success rate of the existing assembly process.

[0202] According to the "Similarity of Assembly Results" in Table 8, it can be seen from the second batch of sample data that the similarity of the assembly results of Example 8 is 0, and the similarity of the assembly results of implementing the existing assembly process (Example 9) is 44.4%. The similarity of the assembly results of Example 8 is higher than that of Example 9.

[0203] According to the "Verifiability of assembly results" in Table 8, it can be seen from the second batch data that the verifiability of the assembly results of Example 8 is 99.54%, and the similarity of the assembly results of implementing the existing assembly process (Example 9) is 95.83%. The verifiability of the assembly results of Example 8 is higher than that of Example 9; at the same time, it can be seen from Tables 5 and 8 that the verifiability of the assembly results of the first batch samples and the second batch samples are higher than their control groups, and the verifiability of the results of the second batch samples is higher than that of the first batch samples, which once again proves that the method proposed in the embodiments of the present application has an advantage in the verifiability of assembly results, that is, the assembled sample sequences have higher accuracy, and the method is stable and can achieve extremely high sequencing accuracy in samples from different batches.

[0204] Example 10 Correlation between the quantification of the second batch of libraries and sequence abundance

[0205]

[0206] According to Table 6, among the 216 samples with a total library amount greater than 0.5 ng, the number of samples in which the sequence with the highest abundance is the target sequence is 205. When the library quantification of 0.5 ng is used as the library quantification quality control standard, the accuracy of directly determining the sequence with the highest abundance as the sample sequence is 205 / 216=94.91%.

[0207] In summary, the NGS sequencing and analysis method proposed in the embodiment of the present invention has the advantages of high assembly success rate, no internal similarity in the assembled sequence, high data verifiability, etc. At the same time, after library quality QC, the target sequence can be quickly determined with high accuracy.

[0208] Industrial Applicability

[0209] The method proposed in the embodiment of the present application has industrial applicability, that is, according to the method proposed in the embodiment of the present application, based on the initial sample input of 5ng, by accurate sample addition (for example, using a high-precision automated sample addition platform), the corresponding output of 0.5ng of the total sample library can be obtained. Based on setting a total sample library threshold of 0.5ng and subsequent data analysis procedures, extremely high sequencing accuracy can be achieved. Based on the method proposed in the embodiment of the present application, each biological sample (for example, a complex sample containing multiple redundant fragments) can be sequenced in large batches with high throughput, and sequencing results comparable to the gold standard Sanger sequencing accuracy can be obtained through NGS sequencing and related process analysis.

[0210] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “clockwise”, “counterclockwise”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0211] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0212] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0213] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0214] In the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the described specific features, structures, materials or characteristics may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0215] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for improving the accuracy of NGS sequencing, characterized in that: include: Performing NGS sequencing on the sample library to obtain initial sequencing data, wherein the library content of the sample library is not less than 0.5 ng; and Analyzing the initial sequencing data to obtain a sample sequence, The sample libraries are one or at least two.

2. The method according to claim 1, characterized in that The method further comprises: Constructing the sample library based on the sample, wherein the initial input amount of the sample is no more than 10 ng, preferably no more than 5 ng; Preferably, the library content of the sample library is not less than 1 ng; Preferably, the sample is a PCR product, a plasmid BAC library or a BIBAC library, a complex engineered bacterium and / or a gene synthesis product. Optionally, the PCR product and / or the gene synthesis product is 200bp-20kb in size, and the plasmid BAC library or BIBAC library or complex engineered bacterium is less than 30kb in size.

3. The method according to claim 2, characterized in that The sample-based construction of the sample library specifically includes: Processing the sample using a Tn5 transposon to obtain sample fragments, wherein the Tn5 transposon comprises a Tn5 transposase and an adapter embedded in the Tn5 transposase, and the sample fragments are connected to the adapter; and The sample fragment is amplified by PCR using primers connected with sample tags to obtain a PCR amplified sequence carrying the sample tags.

4. The method according to claim 1, characterized in that: The analyzing the initial sequencing data to obtain a sample sequence comprises: Filtering the initial sequencing data to remove repeated PCR amplification sequences in the initial sequencing data to obtain first intermediate data; intercepting the first intermediate data to normalize the data amount, so as to obtain second intermediate data; The second intermediate data is assembled to obtain the sample sequence.

5. The method according to claim 4, characterized in that The data volume of the second intermediate data is 200K-1G, preferably 300K-0.5G, more preferably 500K-1M, and most preferably 0.75M.

6. The method according to claim 4, characterized in that The assembling of the second intermediate data comprises: Based on the first depth, performing a first round of assembly on the second intermediate data; In response to obtaining a first-round assembly result, the first-round assembly result is used as the sample sequence.

7. The method according to claim 6, characterized in that In response to not obtaining the first round assembly result, performing a second round of assembly on the second intermediate data based on a second depth; In response to obtaining a second round of assembly results, using the second round of assembly results as the sample sequence, The first depth is greater than the second depth.

8. The method according to claim 7, characterized in that Also includes: Assembling the first intermediate data specifically includes: in response to not obtaining the second round of assembly result, performing a third round of assembly on the first intermediate data based on a third depth; In response to obtaining the third round assembly result, taking the third round assembly result as the sample sequence, wherein the third depth is the same as or different from the second depth; Preferably, the assembly is a k-mer assembly.

9. The method according to claim 8, characterized in that The method further includes: removing redundant sequences in the first round assembly result, the second round assembly result, or the third round assembly result to obtain the sample sequence.

10. The method according to claim 7, characterized in that The method further comprises: The sequence abundance of each sequence in the first round assembly result, the second round assembly result or the third round assembly result after redundancy removal is calculated, and the sequence with the highest sequence abundance among the sequences is taken as the sample sequence.

11. The method according to any one of claims 1 to 10, characterized in that When there are at least two sample libraries, the method further comprises: At least two of the sample libraries are mixed and subjected to NGS sequencing to obtain the initial sequencing data, wherein the library content of each of the at least two sample libraries is not less than 0.5 ng.

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