Nanopore sequencing method and system

Abstract

The present invention relates to the field of sequencing technology, and in particular to a nanopore sequencing method and system. The nanopore sequencing method provided by the present invention selects a sequence with a higher sequencing quality for further correction analysis to obtain more accurate results. At the same time, a real-time incremental update method is adopted to perform multiple correction analyses, and finally multiple results are counted to obtain the most accurate information. In this way, both accuracy and analysis speed are taken into account, and the performance of the correction program is greatly improved. The nanopore sequencing method provided by the present invention has high accuracy, which solves the problem of accuracy in approximate species identification; the repeatability of calibration results solves the problem of unstable results affected by data volume and data quality; the predictability of calibration result quality is achieved, which solves the problem of credibility assessment of species identification; the calibration speed is improved, and the real-time calibration of data is achieved.

Description

Technical Field

[0001] The present invention relates to the field of sequencing technology, and in particular to a nanopore sequencing method and system. Background Art

[0002] Due to the frequent occurrence of infectious diseases caused by pathogenic microorganisms, the threat to public health is becoming increasingly serious. According to the World Health Statistics Report, more than 10 million people die from infectious diseases every year worldwide, accounting for about 30% of the total number of deaths from diseases. The main reasons for the high mortality rate of viral infections are: first, it is highly contagious, and second, the types of microorganisms of infectious diseases are becoming increasingly complex. The threat of common pathogenic microorganisms has not only not been eliminated, but also some drug-resistant strains have appeared. In addition, the emergence of some new pathogens has brought great difficulties to clinical diagnosis and treatment. Taking bloodstream infection as an example, there are 15 million to 19 million cases of sepsis worldwide each year, resulting in about 6 million deaths. The International Management Guidelines for Sepsis and Septic Shock strongly recommend antibiotic treatment within 1 hour after the diagnosis of sepsis and septic shock. The first hour is the golden time for treating sepsis patients, with a survival rate of up to 79.9%. Thereafter, as time goes on, the survival rate decreases by 7.6% per hour. For patients who have not received antibiotic treatment for more than 6 hours, the survival rate drops to 14.5%.

[0003] Nanopore sequencing technology performs sequence determination by analyzing the repression current generated when each nucleic acid molecule passes through the nanopore protein on the bimolecular biomembrane. Since a single molecule passes through the nanopore protein at an extremely fast speed (DNA molecules pass through the pore at a speed of about 450bp / s, and RNA molecules pass through the pore at a speed of about 80bp / s), each molecule is sequenced through a separate nanopore protein, so the sequencing of different molecules is relatively independent and there is no interference between molecules. During the sequencing process, when each molecule passes through the nanopore protein, its sequencing process is completed. In theory, the complete sequence information can be immediately used for subsequent data analysis processes, without waiting for the sequencing of other molecules to be completed or the entire batch of sequencing to be completed before the next step of data analysis can be performed. This feature greatly shortens the output time of the nanopore sequencer from sample to sequencing data, so it is very suitable for sequencing projects with high time requirements, such as clinical diagnosis of pathogenic microorganism infections.

[0004] Although nanopore sequencers have the advantage of rapid sequencing, the speed at which DNA passes through the nanopore is too fast, resulting in a lower accuracy of the bases produced by sequencing than the second-generation sequencing platform, with an average single-base accuracy of only about 95%. At the same time, there are certain similarities in genes between pathogenic microorganisms, especially between different species in the same genus. For example, the similarity of the 16s rRNA gene between Escherichia coli and Escherichia ferguson is as high as 99.9%. Therefore, when using nanopore sequencer data directly for data comparison and microbial identification, a large number of incorrect comparisons are often generated, which in turn leads to species identification errors. This greatly limits the application of nanopore sequencing technology in clinical pathogen diagnosis.

[0005] Since the quality of data generated by each nucleic acid when passing through the nanopore varies, even when multiple nucleic acid molecules with exactly the same sequence are tested, there are differences in the sequence of each base in the sequencing reads data generated. However, through the analysis and research of big data, although nanopore sequencing data carries certain errors, these errors also have certain rules and are a kind of "random" error. In theory, these sequencing errors can be repaired through bioinformatics calculations, and some sequencing data can be compared to more accurate consistency data. Then, these highly accurate consistency sequences are used for database comparison, which can greatly improve the accuracy of microbial species identification.

[0006] There are two main approaches to improving the accuracy of species identification in nanopore sequencing data: the first is to improve the original accuracy of sequencing data, that is, the filtering threshold of the Q value, to obtain more accurate raw data, thereby improving the accuracy of data comparison; the second is to align the data by multiple sequences, randomly use one of the sequences as the calibration template, compare the base distribution of each single-base sequence site on the calibration template sequence in all reads according to the other reads sequences, select the base with the highest proportion to calibrate to form a consensus sequence, and then use the consensus sequence for species identification. However, although the first solution can reduce some erroneously aligned data, it will also greatly reduce the amount of reads data that can be used for subsequent analysis, which may cause the sequencing data to be completely filtered out and produce false negatives in applications. More importantly, this solution does not fundamentally improve the accuracy of sequencing data, and it is still difficult to distinguish highly similar closely related microorganisms. The second solution introduces the idea of ​​data correction, but there are still several major problems: 1. The accuracy of the data obtained after calibration can only reach below 99.9% (i.e., the quality value Q30), and it cannot be further improved to 99.99% (i.e., the quality value Q40). There is still the problem of identification errors for highly similar closely related species. Because although the errors in nanopore sequencing data have a certain randomness, they are not random errors that completely conform to the normal distribution, and have a certain error model, that is, the preference of errors. In the existing calibration logic, only the calibration of the error site and the formation of the consensus sequence do not take this preference into account, so some errors cannot be accurately calibrated; 2. The bases and quality values ​​of the consensus sequence after calibration are relatively random and have poor repeatability. Using the same sequencing data, the species identification results generated after repeated calibration results may be different. 3. The quality value of the calibrated data cannot be evaluated because in actual use, the number of reads obtained by sequencing each microorganism is different, that is, the data that can be used for calibration is different. In theory, the calibration data obtained by calibrating data of different quantities and different qualities also have quality differences. Therefore, the existing means cannot evaluate the quality value of the calibration data for species identification, that is, the reliability. 4. The calibration speed is slow. The existing process calibration process will use all the reads of the species, which will greatly increase the data calibration time and cannot achieve real-time analysis while sequencing. For results with high data output, it will further lead to serious delays in data analysis time.

[0007] Therefore, there is an urgent need to develop a nanopore sequencing method with high accuracy, which can solve the problem of accuracy in identification of similar species and provide stable results. Summary of the invention

[0008] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, one object of the present invention is to provide a nanopore sequencing method. The method has high accuracy, solving the problem of approximate species identification accuracy; calibration result repeatability, solving the problem of unstable results, affected by data volume and data quality; achieving predictability of calibration result quality, solving the problem of species identification credibility assessment; improving calibration speed, realizing real-time calibration of data.

[0009] To this end, the first aspect of the present invention provides a nanopore sequencing method. According to an embodiment of the present invention, the nanopore sequencing method comprises:

[0010] (1) subjecting nucleic acid samples to continuous nanopore sequencing reactions;

[0011] (2) collecting a first batch of sequencing data at a first time point of the continuous nanopore sequencing reaction, wherein the sequencing data consists of a plurality of sequencing reads;

[0012] (3) pre-classifying the first batch of sequencing data to obtain the pre-classified first batch of sequencing data and aggregating the pre-classified first batch of sequencing data into a first sequence pool;

[0013] (4) selecting a first sequencing read combination based on the sequencing quality Q values ​​of the sequencing reads in the first sequence pool;

[0014] (5) inputting the first sequencing read combination into sequencing data correction software to output a first correction result;

[0015] (6) collecting a second batch of sequencing data at a second time point of the continuous nanopore sequencing reaction, wherein the second batch of sequencing data consists of sequencing data generated between the first time point and the second time point;

[0016] (7) pre-classifying the second batch of sequencing data to obtain the pre-classified second batch of sequencing data, and aggregating the pre-classified second batch of sequencing data and the data in the first sequence pool into a second sequence pool;

[0017] (8) selecting a second sequencing read combination based on the sequencing quality Q values ​​of the sequencing reads in the second sequence pool;

[0018] (9) inputting the second sequencing read combination into sequencing data correction software to output a second correction result;

[0019] (10) From the start to the end of the nanopore sequencing reaction, including multiple time points, steps (6)-(9) are repeated at multiple different time points, all calibration results are counted, and the quality of all calibration results is ranked, and the highest quality is selected as the final calibration result.

[0020] The inventors found that in view of the relatively high error rate of nanopore sequencing itself, sequences with higher sequencing quality can be selected for further correction analysis to obtain more accurate results. At the same time, a real-time incremental update method is adopted to perform multiple correction analyses, and finally multiple results are counted to obtain the most accurate information. In this way, both accuracy and analysis speed are taken into account, and the performance of the correction program is greatly improved. The nanopore sequencing method provided by the present invention performs multiple rounds of correction analysis as the nanopore sequencing reaction proceeds, and adopts a real-time incremental update method. Finally, when the sequencing is completed, the correction results of multiple rounds are summarized, and the most reliable result is selected as the final result. The nanopore sequencing method provided by the present invention has high accuracy, which solves the problem of accuracy of approximate species identification; the repeatability of the calibration results solves the problem of unstable results and is affected by the amount of data and the quality of the data; the quality predictability of the calibration results is achieved, and the problem of credibility assessment of species identification is solved; the calibration speed is improved, and the problem of real-time calibration of data is realized.

[0021] According to some embodiments of the present invention, the nucleic acid sample is a biological sample suspected of containing a pathogen.

[0022] According to some embodiments of the present invention, the first sequencing read combination and the second sequencing read combination each independently include no more than 60 sequencing reads.

[0023] According to some embodiments of the present invention, the first sequencing read combination and the second sequencing read combination are selected according to the sequencing quality Q value from high to low.

[0024] According to some embodiments of the present invention, the sequencing data correction software comprises a combination of Racon and Medaka.

[0025] The real-time output of sequencing data is one of the characteristics of nanopore sequencing. As the sequencing time increases, the number of sequences in a single sequence group and the sequences with higher Q values ​​are also increasing, and the quality of the sequences that can be corrected is also constantly improving. Therefore, in the sequencing process, as the data is continuously output, multiple corrections are performed, and finally the multiple correction results are merged to obtain the optimal result. This not only makes full use of the sequencing time for analysis, but also obtains more accurate correction results through multiple corrections. The present invention combines the correction sequence selection algorithm, selects the Racon+Medaka correction tool, the correction prediction accuracy algorithm and the real-time correction algorithm strategy, and can complete the correction analysis within 10 minutes after the sequencing is completed, and the correction accuracy reaches more than 99.6%.

[0026] According to some embodiments of the present invention, the median sequencing quality Q value of the sequencing reads of the first sequencing read combination and the second sequencing read combination is not less than 13, preferably not less than 14.

[0027] According to some embodiments of the present invention, the accuracy of the correction result is further predicted based on the sequencing quality Q value of the correction result obtained at each time point selected in the nanopore sequencing reaction, the number of sequencing reads in the sequencing read combination obtained at each time point, and the quality Q value of the sequencing reads in the sequencing read combination obtained at each time point.

[0028] According to some embodiments of the invention, the accuracy is predicted by a machine learning model.

[0029] According to some embodiments of the present invention, further comprising:

[0030] The biological origin of the nucleic acid sample in the sample is predicted based on the final calibration result.

[0031] According to some embodiments of the present invention, the first time point is no more than 30 minutes from the start of the nanopore sequencing reaction, and the selection of each time point after the first time point meets the following requirements: the interval between each time point and the previous time point is no more than 1 hour, until the nanopore sequencing reaction is completed.

[0032] According to some embodiments of the present invention, the pre-classification is determined by comparing the sequencing data with reference data.

[0033] A second aspect of the present invention provides a nanopore sequencing system. According to some embodiments of the present invention, the nanopore sequencing system comprises:

[0034] A sequencing module, wherein the sequencing module is used to perform continuous nanopore sequencing reactions on nucleic acid samples;

[0035] a first sequence selection module, the first sequence selection module being used to collect a first batch of sequencing data at a first time point of the continuous nanopore sequencing reaction, pre-classify the first batch of sequencing data, obtain the pre-classified first batch of sequencing data and aggregate them into a first sequence pool, and select a first sequencing read combination based on sequencing quality Q values ​​of sequencing reads in the first sequence pool, wherein the sequencing data consists of a plurality of sequencing reads;

[0036] A first data correction module, the first data correction module is connected to the first sequence selection module, and the first data correction module is used to input the first sequencing read segment combination into sequencing data correction software to output a first correction result;

[0037] a second sequence selection module, the second sequence selection module being used to collect a second batch of sequencing data at a second time point of the continuous nanopore sequencing reaction, pre-classify the second batch of sequencing data to obtain the pre-classified second batch of sequencing data, and aggregate the pre-classified second batch of sequencing data and the data in the first sequence pool into a second sequence pool, and select a second sequencing read combination based on the sequencing quality Q value of the sequencing reads in the second sequence pool, wherein the second batch of sequencing data consists of sequencing data generated between the first time point and the second time point;

[0038] A second data correction module, the second data correction module is used to input the second sequencing read segment combination into sequencing data correction software to output a second correction result;

[0039] The final calibration result output module is used to count all calibration results at multiple different time points from the start to the end of the nanopore sequencing reaction, sort the quality of all calibration results, and select the highest quality as the final calibration result.

[0040] According to some embodiments of the present invention, the nucleic acid sample is a biological sample suspected of containing a pathogen.

[0041] According to some embodiments of the present invention, the first sequencing read combination and the second sequencing read combination each independently include no more than 60 sequencing reads.

[0042] According to some embodiments of the present invention, the first sequencing read combination and the second sequencing read combination are selected according to the sequencing quality Q value from high to low.

[0043] According to some embodiments of the present invention, the sequencing data correction software comprises a combination of Racon and Medaka.

[0044] The inventors investigated the existing nanopore sequencing data correction tools in academia and selected four software tool combinations, namely Racon, canu, Medaka, and Racon+Medaka. These tools can input an initial template sequence and multiple correction library sequences and output a correction result sequence. If the sequencing data of the standard bacteria is input and the correction result sequence is compared with the standard bacteria sequence, the accuracy of this correction can be obtained. By arranging and combining different initial template sequence Q values, the number of correction library sequences, and the average Q value of the correction library sequence in the standard bacteria database, each combination is run 5 times, thereby selecting a total of about 36,000 sets of input data combinations, and performing about 144,000 correction tests for the four tools.

[0045] Through the above 140,000 sets of tests, we can draw the following conclusions: 1. Improving the Q value of the initial template sequence and the average Q value of the correction library sequence can improve the correction accuracy; 2. Among the four correction tools, the Racon+Medaka tool combination has the best correction effect; 3. Increasing the number of correction library sequences can improve the correction accuracy, but after 40 sequences, the contribution to the correction accuracy is small; 4. When the average Q value of the correction library sequence is 13-14, the Racon+Medaka correction accuracy can be above 99.6%. Considering that the average accuracy of the nanosequencing output sequence is around 13-14, it can be roughly considered that the average accuracy of this correction tool is above 99.6%.

[0046] According to some embodiments of the present invention, the sequencing quality Q values ​​of the sequencing reads of the first sequencing read combination and the second sequencing read combination are both not less than 13, preferably not less than 14.

[0047] According to some embodiments of the present invention, the system further includes an accuracy prediction module, which is used to predict the accuracy of the correction result based on the sequencing quality Q value of the correction result obtained at each time point selected in the nanopore sequencing reaction, the number of sequencing reads in the sequencing read combination obtained at each time point, and the quality Q value of the sequencing reads in the sequencing read combination obtained at each time point.

[0048] According to some embodiments of the invention, the accuracy is predicted by a machine learning model.

[0049] According to some embodiments of the present invention, the system further comprises a prediction module for predicting the biological origin of the nucleic acid sample in the sample based on the final correction result.

[0050] According to some embodiments of the present invention, the first time point is no more than 30 minutes from the start of the nanopore sequencing reaction, and the selection of each time point after the first time point meets the following requirements: the interval between each time point and the previous time point is no more than 1 hour, until the nanopore sequencing reaction is completed.

[0051] According to some embodiments of the present invention, the pre-classification is determined by comparing the sequencing data with reference data.

[0052] A third aspect of the present invention provides an electronic device for nanopore sequencing. According to some embodiments of the present invention, the electronic device includes a memory, a processor;

[0053] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the nanopore sequencing method described in the first aspect.

[0054] A fourth aspect of the present invention provides a computer-readable storage medium. According to some embodiments of the present invention, the computer-readable storage medium stores a computer program, and when the program is executed by a processor, the nanopore sequencing method described in the first aspect is implemented.

[0055] The nanopore sequencing method provided by the present invention has the following advantages: 1. The correction effect is more accurate. First, the input sequence for correction has been optimized, and a sequence with a higher Q value and relatively high accuracy is selected for correction, so the result is naturally more accurate. Secondly, a real-time multiple correction strategy is adopted to integrate multiple correction results to ensure the stability and reliability of the correction result. Finally, the correction result has a parameter of prediction accuracy, which can be used to evaluate the correction result and greatly improve the reliability of the result; 2. The running speed is fast. The present invention adopts a real-time correction strategy, which can perform correction analysis while sequencing. Each time the analysis is performed, the result of the previous analysis is combined to perform an incremental update. Compared with performing calibration analysis after sequencing is completed, the speed is greatly improved.

[0056] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0058] Figure 1 is a schematic diagram of a nanopore sequencing method according to an embodiment of the present invention;

[0059] Figure 2 is a schematic diagram of predicting the accuracy of a second correction result in a nanopore sequencing method according to an embodiment of the present invention;

[0060] Figure 3 is a schematic diagram of predicting the biological source of a nucleic acid sample in the sample using a nanopore sequencing method according to an embodiment of the present invention;

[0061] Figure 4 is a schematic diagram of a device for a nanopore sequencing method according to an embodiment of the present invention;

[0062] Figure 5 is a schematic diagram of a device for predicting the accuracy of a second correction result in a nanopore sequencing method according to an embodiment of the present invention;

[0063] Figure 6 is a schematic diagram of a device for predicting the biological source of a nucleic acid sample in the sample using a nanopore sequencing method according to an embodiment of the present invention;

[0064] Figure 7 is a schematic diagram of a nanopore sequencing method according to a specific embodiment of the present invention;

[0065] Figure 8 The relationship between the sequence Q value and the evolutionary distance between the sequence and the standard sequence in Example 1 is shown, where the y-axis is the sequence Q value and the x-axis is the evolutionary distance between the sequence and the standard sequence. A single point is a single sequence obtained by nanopore sequencing;

[0066] Fig. 9 The relationship between the sequence Q value and the recognition value in Example 1 is shown, the Y axis is the sequence Q value, and the X axis is the recognition value of the sequence and the standard sequence by blastn comparison;

[0067] Fig.10 The relationship between the sequence Q value and accuracy of different standard bacteria in Example 2 is shown;

[0068] Fig.11 The effect of different standard bacteria and different calibration software on calibration accuracy in Example 2 is shown;

[0069] Fig.12 The relationship between the number of calibration library sequences and calibration accuracy in Example 2 is shown. DETAILED DESCRIPTION

[0070] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0071] 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.

[0072] 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 connection; it can be a mechanical connection or an electrical connection; 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.

[0073] In one aspect of the present invention, a nanopore sequencing method is provided. Figure 1 ,include:

[0074] S100, subjecting the nucleic acid sample to a continuous nanopore sequencing reaction; S200, at a first time point of the continuous nanopore sequencing reaction, collecting a first batch of sequencing data, wherein the sequencing data is composed of a plurality of sequencing reads; S300, pre-classifying the first batch of sequencing data to obtain the first batch of sequencing data after pre-classification; S400, aggregating the first batch of sequencing data after pre-classification into a first sequence pool; S500, selecting a first sequencing read combination based on the sequencing quality Q value of the sequencing reads in the first sequence pool; S600, inputting the first sequencing read combination into a sequencing data correction software to output a first correction result; S700, at a second time point of the continuous nanopore sequencing reaction, collecting a second batch of sequencing data, wherein the second batch of sequencing data is composed of the first time point and The sequencing data generated between the second time points; S800, pre-classifying the second batch of sequencing data to obtain the pre-classified second batch of sequencing data; S900, aggregating the pre-classified second batch of sequencing data and the data in the first sequence pool into a second sequence pool; S1000, selecting a second sequencing read combination based on the sequencing quality Q value of the sequencing reads in the second sequence pool; S1100, inputting the second sequencing read combination into the sequencing data correction software to output a second correction result; S1200, from the start to the end of the nanopore sequencing reaction, including multiple time points, repeating steps S700-S1100 at multiple different time points, counting all correction results, and ranking the quality of all correction results, and selecting the highest quality as the final correction result.

[0075] According to an embodiment of the present invention, the nanopore sequencing method has high accuracy, which solves the problem of approximate species identification accuracy; the calibration result repeatability solves the problem of unstable results affected by data volume and data quality; the calibration result quality predictability solves the problem of species identification credibility assessment; the calibration speed is improved, and the real-time calibration of data is realized.

[0076] According to an embodiment of the present invention, the nucleic acid sample is a biological sample suspected of containing a pathogen. According to an embodiment of the present invention, the first sequencing read combination and the second sequencing read combination each independently include no more than 60 sequencing reads, for example, 30, 40, 50, 60, etc.

[0077] According to an embodiment of the present invention, the first sequencing read segment combination and the second sequencing read segment combination are selected by ranking according to the sequencing quality Q value (sequencing quality value of a single sequence) from high to low according to the size of the Q value.

[0078] According to an embodiment of the present invention, the sequencing data correction software includes but is not limited to a combination of Racon and Medaka. The inventors have summarized a set of scientific correction input sequence selection algorithms, found the best correction tool combination, and can obtain the prediction accuracy of the correction result through the parameters of the input sequence. The correction sequence selection algorithm not only improves the accuracy of the correction, but also for a given sequence population, the sequences selected for correction are fixed, which solves the randomness of the correction results caused by the random selection of the initial template sequence in the previous method. In addition, since the maximum value of the number of correction library sequences is limited, the calculation amount of a single correction is greatly reduced, and the running speed is improved.

[0079] After sequencing the pathogenic microorganisms using a nanopore sequencer, a round of comparison is performed on the real-time generated sequence and the database. The sequences are preliminarily classified according to the comparison results, sorted according to the sequence Q value, and the sequence with the largest Q value is selected as the initial template sequence. Among the remaining sequences, the first N (N is less than 50) sequences with the Q value are selected as the correction library sequence. The initial template sequence and the correction library sequence are input into the Racon software, and the first round of template sequence is obtained after the first round of correction. The first round template sequence and the correction library sequence are then corrected using the Medaka software to obtain a consensus sequence. The consensus sequence is compared with the alignment library, and finally the final correction result of this batch of sequences is obtained.

[0080] According to an embodiment of the present invention, the median sequencing quality Q value of the sequencing reads of the first sequencing read combination and the second sequencing read combination is not less than 13, preferably not less than 14.

[0081] According to an embodiment of the present invention, the accuracy of the correction result obtained at each time point selected in the nanopore sequencing reaction is predicted. According to an embodiment of the present invention, the accuracy of the correction result is predicted based on the sequencing quality Q value of the correction result obtained at each time point selected in the nanopore sequencing reaction, the number of sequencing reads in the sequencing read combination obtained at each time point, and the quality Q value of the sequencing reads in the sequencing read combination obtained at each time point. Here, the accuracy prediction of the second data correction result is taken as an example, such as Figure 2 The nanopore sequencing method further includes step S1300, based on the sequencing quality Q value of the second calibration result, the number of sequencing reads in the second sequencing reads, and the quality Q value of the sequencing reads in the second sequencing read combination, the accuracy of the second calibration result is predicted. Before the sequencing is completed, a third time point, a fourth time point, a fifth time point, etc. can also be set, and at each time point according to Figure 2 The method steps can all achieve an accurate prediction of the correction result obtained at that time point.

[0082] According to an embodiment of the present invention, the accuracy prediction can be achieved by any prediction model known in the art. According to a specific embodiment of the present invention, the accuracy is predicted by a machine learning model.

[0083] According to an embodiment of the present invention, Figure 3 The nanopore sequencing method further includes step S1400, predicting the biological source of the nucleic acid sample in the sample based on the final correction result.

[0084] According to an embodiment of the present invention, the first time point is no more than 30 minutes from the start of the nanopore sequencing reaction, and each time point after the first time point is selected to meet the following requirements: the interval between each time point and the previous time point is no more than 1 hour, until the nanopore sequencing reaction is completed. Specifically, Figure 7 As shown, a more specific implementation scheme of the nanopore sequencing method of the present invention is demonstrated, the first time point is 0.5 hours from the start of sequencing, the second time point is 1 hour from the start of sequencing, the third time point is 1.5 hours from the start of sequencing, and more time points are selected as needed until the sequencing is completed.

[0085] According to an embodiment of the present invention, the pre-classification is determined by comparing the sequencing data with the reference data.

[0086] In another aspect of the present invention, the present invention provides a nanopore sequencing system, such as Figure 4 , the sequencing system comprises:

[0087] A sequencing module 10000, wherein the sequencing module 10000 is used to perform continuous nanopore sequencing reactions on nucleic acid samples;

[0088] a first sequence selection module 20000, the first sequence selection module 20000 being connected to the sequencing module 10000, and the first sequence selection module 20000 being used to collect a first batch of sequencing data at a first time point of the continuous nanopore sequencing reaction, pre-classify the first batch of sequencing data, obtain the pre-classified first batch of sequencing data and aggregate them into a first sequence pool, and select a first sequencing read combination based on sequencing quality Q values ​​of sequencing reads in the first sequence pool, wherein the sequencing data is composed of a plurality of sequencing reads;

[0089] A first data correction module 30000, the first data correction module 30000 is connected to the first sequence selection module 20000, and the first data correction module 30000 is used to input the first sequencing read segment combination into sequencing data correction software to output a first correction result;

[0090] a second sequence selection module 40000, the second sequence selection module 40000 being connected to the sequencing module 10000, and the second sequence selection module 40000 being used to collect a second batch of sequencing data at a second time point of the continuous nanopore sequencing reaction, pre-classify the second batch of sequencing data to obtain the pre-classified second batch of sequencing data, and aggregate the pre-classified second batch of sequencing data and the data in the first sequence pool into a second sequence pool, and select a second sequencing read combination based on the sequencing quality Q value of the sequencing reads in the second sequence pool, wherein the second batch of sequencing data is composed of sequencing data generated between the first time point and the second time point;

[0091] A second data correction module 50000, the second data correction module 50000 is connected to the second sequence selection module 40000, and the second data correction module 50000 is used to input the second sequencing read segment combination into sequencing data correction software to output a second correction result;

[0092] The final calibration result output module 60000 is used to count all calibration results at multiple different time points from the start to the end of the nanopore sequencing reaction, sort the quality of all calibration results, and select the highest quality as the final calibration result.

[0093] According to an embodiment of the present invention, the first sequencing read combination and the second sequencing read combination independently include no more than 60 sequencing reads. According to an embodiment of the present invention, the first sequencing read combination and the second sequencing read combination are selected according to the direction of the sequencing quality Q value from high to low. According to an embodiment of the present invention, the sequencing data correction software includes a combination of Racon and Medaka. According to an embodiment of the present invention, the sequencing quality Q values ​​of the sequencing reads of the first sequencing read combination and the second sequencing read combination are not less than 13, preferably not less than 14.

[0094] According to an embodiment of the present invention, the system further includes an accuracy prediction module connected to the data correction module at each time point, and the accuracy prediction module is used to predict the accuracy of the correction result based on the sequencing quality Q value of the correction result obtained at each time point selected in the nanopore sequencing reaction, the number of sequencing reads in the sequencing read combination obtained at each time point, and the quality Q value of the sequencing reads in the sequencing read combination obtained at each time point. Here, the second data correction module is taken as an example, Figure 5The system further includes an accuracy prediction module 70000, which is connected to the second data correction module 50000, and the accuracy prediction module 70000 is used to predict the accuracy of the second correction result based on the sequencing quality Q value of the second correction result, the number of sequencing reads in the second sequencing reads, and the quality Q value of the sequencing reads in the second sequencing read combination.

[0095] According to an embodiment of the present invention, the accuracy is predicted by a machine learning model.

[0096] According to an embodiment of the present invention, Figure 6 It further comprises a prediction module 80000, wherein the prediction module 80000 is connected to the final correction result output module 60000, and the prediction module 80000 is used to predict the biological source of the nucleic acid sample in the sample based on the final correction result.

[0097] According to an embodiment of the present invention, the first time point is no more than 30 minutes from the start of the nanopore sequencing reaction, and each time point after the first time point is selected to meet the following requirements: the interval between each time point and the previous time point is no more than 1 hour, until the nanopore sequencing reaction is completed. Specifically, Figure 7 As shown, a more specific embodiment of the nanopore sequencing method of the present invention is shown, the first time point.

[0098] The present invention is described below with reference to specific embodiments. It should be noted that these embodiments are merely illustrative and do not limit the present invention in any way.

[0099] Reagents and consumables:

[0100] Nucleic acid extraction kit (Zhenxi Medical); library construction kit (Zhenxi Medical); sequencing chip (Oxford Nanopore); blood plate (Huankai Microbiology);

[0101] Experimental equipment:

[0102] Tissue grinding homogenizer (TGrinder H24 OSE-TH-01); Qubit; PCR instrument; sequencer (Oxford Nanopore, GridION)

[0103] Library construction and sequencing

[0104] 1. Strain treatment

[0105] a) The strain was collected by the company and stored in 20% glycerol. When used, it was thawed from a -80 refrigerator and 1 μl was pipetted and spread on a plate.

[0106] b) The plate was placed in a 37°C incubator for 48 hours and a portion was scraped for subsequent sequencing.

[0107] List of standard bacterial strains

[0108]

[0109]

[0110] 2. Nucleic Acid Extraction and Purification

[0111] Nucleic acid extraction:

[0112] a) Add 150 μl of Lysis 1, 150 μl of Lysis 2 and 30 μl of Nucleic Acid Protectant to the grinding tube containing the sample and vortex to mix.

[0113] b) The mixed sample was mixed using TGrinder H24 tissue grinding homogenizer (OSE-TH-01)

[0114] c) After grinding, centrifuge (7000 rpm, 3 min) and add 20 μl of proteinase K to the grinding tube. Vortex and mix well, then place in a metal bath and incubate at 65°C for 10 min.

[0115] d) After the incubation, shake gently to mix, centrifuge briefly, take 550 μl of the liquid into the EP tube after packaging, and place it at room temperature for 10 minutes, vortexing and mixing for 30 seconds every 5 minutes.

[0116] e) Place the centrifuge tube on the magnetic rack for 5 minutes. When the magnetic beads are completely adsorbed, carefully remove the liquid with a pipette.

[0117] f) Add 750 μl of rinse solution 1 and vortex for 2 minutes to fully suspend the magnetic beads.

[0118] g) Place the centrifuge tube on the magnetic rack for 1 minute. When the magnetic beads are completely adsorbed, carefully remove the liquid with a pipette.

[0119] h) Add 750 μl of rinse solution 1 and vortex for 2 minutes to fully suspend the magnetic beads.

[0120] i) Place the centrifuge tube on the magnetic stand for 1 minute. When the magnetic beads are completely adsorbed, carefully remove the liquid with a pipette.

[0121] j) Add 750 μl of rinse solution 2 and vortex for 2 minutes to fully suspend the magnetic beads.

[0122] k) Place the centrifuge tube on the magnetic rack for 1 minute. When the magnetic beads are completely adsorbed, carefully remove the liquid with a pipette.

[0123] l) Add 750 μl of rinse solution 2, vortex mix for 2 minutes to fully suspend the magnetic beads, and briefly centrifuge to remove droplets on the inner wall of the tube cap.

[0124] m) Place the centrifuge tube on the magnetic rack for 1 minute. When the magnetic beads are completely adsorbed, carefully remove the liquid with a pipette.

[0125] n) Centrifuge briefly again and use a pipette to carefully remove the remaining liquid at the bottom of the tube when the magnetic beads are completely adsorbed

[0126] o) Place the centrifuge tube on a magnetic rack and dry at room temperature for 3-5 minutes

[0127] p) Add 52 μl of elution buffer, vortex mix for 2 seconds, and incubate at 56°C for 5 minutes, vortex mix every 2 minutes to fully elute the nucleic acid.

[0128] q) Place the centrifuge tube on the magnetic rack and let it stand for 2 minutes. When the magnetic beads are completely adsorbed, carefully transfer 50 μl of nucleic acid solution to a new EP tube.

[0129] Nucleic acid purification

[0130] a) Mix 1 times the volume of the sample with magnetic beads and nucleic acid, shake and mix thoroughly, and let stand at room temperature for 10 minutes; centrifuge briefly, place on a magnetic rack, wait for 2 minutes until clear, and discard the supernatant to avoid touching the magnetic beads

[0131] b) Add 750 μl 70% ethanol to wash, wait for 30 seconds, and discard the supernatant to avoid touching the magnetic beads.

[0132] c) Cover the EP tube cap, centrifuge briefly, and remove the liquid at the bottom of the tube.

[0133] d) Centrifuge briefly to remove the liquid on the wall, insert the magnetic rack, and wait for 1 minute

[0134] e) Open the tube cap, suck out the remaining ethanol and discard it, then wait for 1 minute (wait for the ethanol to evaporate completely)

[0135] f) Add 41 μl of DEPC water to the EP tube, shake and mix, let stand at room temperature for 2 min, centrifuge for 5 s, and place on a magnetic rack to clarify. Transfer 40 μl of nucleic acid to a new EP tube, and avoid absorbing the magnetic beads during the transfer process.

[0136] 3. Amplification

[0137] Amplification system:

[0138]

[0139] Amplification program settings

[0140]

[0141] 4. Sanger sequencing

[0142] a) Take 5ul of the amplified product and send it to Qingke Bio for first-generation Sanger sequencing.

[0143] 5. Library Construction

[0144] Mixed samples

[0145] a) Mix the PCR products by shaking and centrifuging at 3000 rpm for 2 min

[0146] b) After centrifugation, add 1 μl of product to the prepared 199 μl qubit working solution in sequence according to the number, mix briefly and centrifuge to detect the concentration of the amplified product

[0147] c) Calculate the input volume based on the product concentration according to the input amount of 15 ng per sample

[0148] d) Mix the EP tube and centrifuge it instantaneously, and measure the volume of the liquid in the EP tube.

[0149] e) Add 0.8 times the volume of the sample to the EP tube, mix and shake for 3 minutes, place on a magnetic rack until clear, discard the supernatant, and be careful not to absorb the magnetic beads

[0150] f) Add 800 μl 70% ethanol to wash, wait for 10 seconds, discard the supernatant, avoid touching the magnetic beads, discard the supernatant, avoid touching the magnetic beads

[0151] g) Repeat the above steps once

[0152] h) Centrifuge briefly to remove the liquid on the wall, insert the magnetic rack, and wait for 1 minute

[0153] i) Open the tube cap, suck out the remaining ethanol and discard it, then wait for 1 minute.

[0154] j) Add 52 μl DNase / RNase-free H2O water to the EP tube, shake for 2 minutes, and briefly centrifuge and place on a magnetic rack for 2 minutes until the solution is completely clear.

[0155] k) Transfer 51 μl to a new PCR tube

[0156] End repair The end repair system is as follows:

[0157]

[0158] a) Simply shake and centrifuge the PCR tube, put it into the PCR instrument to run the program, and after the program is completed, take out the PCR tube, simply mix and centrifuge it, add it to a new EP tube, and then add 1 volume of magnetic beads for purification

[0159] b) Add 1 times the volume of the sample to the EP tube and shake for 3 minutes. Place it on a magnetic rack until it is clear and discard the supernatant. Be careful not to absorb the magnetic beads.

[0160] c) Add 800 μl 70% ethanol to wash, wait for 10 seconds, discard the supernatant, avoid touching the magnetic beads (if the solution is not clear, extend the magnetic adsorption time until the solution is completely clear), discard the supernatant, avoid touching the magnetic beads

[0161] d) Repeat the above steps once

[0162] e) Centrifuge briefly to remove the liquid on the wall, insert the magnetic rack, and wait for 1 minute

[0163] f) Open the tube cap, suck out the remaining ethanol and discard it, then wait for 1 minute.

[0164] g) Add 50 μl of DNase / RNase-free H2O to the EP tube, shake for 2 minutes, centrifuge briefly onto a magnetic rack, and transfer 49 μl to a new EP tube.

[0165] Connector connection

[0166]

[0167] a) Simply shake and centrifuge the EP tube and place it at room temperature for 20 minutes. After the waiting time is over, add 0.8 times the sample volume of magnetic beads for purification

[0168] b) Mix 0.8 times the volume of the sample magnetic beads and EP tube DNA and shake them for 3 minutes, then place them on a magnetic rack until they are clear and discard the supernatant.

[0169] c) Add 200 μl SFB buffer for washing, mix for 10 seconds, centrifuge briefly, let stand for 3 minutes, and discard the supernatant

[0170] d) Repeat the washing with 200 μl SFB buffer, mix for 10 seconds, centrifuge briefly, let stand for 3 minutes, and discard the supernatant.

[0171] e) Centrifuge again, place on the magnetic rack until adsorbed, and discard the remaining liquid

[0172] f) Add 14 μl of EB buffer to the EP tube, gently tap the EP tube to mix, and place at room temperature for 10 minutes

[0173] g) Simply centrifuge and place on a magnetic rack, wait for 1 minute to elute 13 μl of nucleic acid, transfer to a new EP tube 6, and sequence on the machine

[0174] Prepare library system

[0175]

[0176]

[0177] a) The total amount of input in this step should be ≤50ng. If it is less than 50g, all of it should be put into the sequencing machine. If it is greater than 50ng, use the concentration value of 50ng / Qubit to calculate the required input volume;

[0178] b) Prepare Running buffer;

[0179] c) Open the priming port of the sequencing chip and use a 1 ml pipette to draw a small amount of liquid out of the priming port to ensure that there are no bubbles in the priming port;

[0180] d) Add 800 μl of Running buffer to the wells, close the priming port and wait for 5 minutes;

[0181] e) Open the priming port and SpotON sample port, use a 1ml pipette to draw a small amount of liquid to remove bubbles, then add 200μl of Running buffer to the priming port, and then add 75μl of sample to the spotON sample port. Close the priming port and SpotON sample port;

[0182] f) Aspirate the waste liquid from waste port 1 until the yellow liquid reaches waste port 1;

[0183] g) placing the sequencing chip into the sequencing card slot of the sequencer;

[0184] h) Open the Minknow software on the sequencer, select LSK110 as the library construction kit, select High-Accuary Basecalling as the Basecalling mode, and start sequencing.

[0185] 7. Data Analysis

[0186] Data pre-processing steps:

[0187] a) Split the barcode. Different samples have different barcode sequences, which can be split using the porechop software to obtain the fastq sequence of a single sample.

[0188] b) Sequence alignment: Align individual samples with the company's existing library.

[0189] c) Use the comparison results as input for subsequent analysis

[0190] Example 1: The larger the Q value, the higher the sequencing accuracy

[0191] The sequence after splitting a single standard bacteria sample (Salmonella enteritidis-1) was compared with the Sanger sequencing sequence result of this sample. Two methods were used for comparison:

[0192] i. Use fasttree software to obtain the evolutionary distance between sequencing reads and Sanger sequencing sequences. The smaller the data, the closer the two are.

[0193] ii. Use blastn software to compare the sequencing reads with the Sanger sequencing sequence. The higher the Identity value of the comparison result, the closer the two are.

[0194] Figure 8 In the figure, the y-axis is the sequence Q value, and the x-axis is the evolutionary distance between the sequence and the standard sequence. A single point is a single sequence obtained by nanopore sequencing. The larger the evolutionary distance value, the greater the difference between the sequence and the standard sequence.

[0195] Fig. 9 In the figure, the Y axis is the sequence Q value, and the X axis is the recognition value of the sequence and the standard sequence by blastn comparison. The larger the recognition value, the closer the sequence is to the standard sequence. The larger the Q value, the higher the accuracy of the sequence output by nanopore sequencing.

[0196] according to Figure 7 and 8 The results in the figure show that the two different comparison methods both show that the larger the Q value of the sequencing reads, the higher the similarity between the sequencing results and the Sanger sequencing sequence, and the higher the sequencing accuracy.

[0197] Example 2 Study on the Correction Accuracy of Four Correction Tools under a Large Number of Input Sequence Combinations

[0198] 1. Four standard bacteria with large amounts of data were selected for analysis, namely: Shigella flexneri, Acinetobacter baumannii, Klebsiella oxytoca, and Staphylococcus hominis;

[0199] 2. The input of the calibration software is an initial template sequence and multiple calibration library sequences. For the sequencing data of the standard bacteria, according to the Q value of the initial template sequence, the average Q value of the calibration library sequence, and the number of calibration library sequences, the permutations are performed to try to arrange all the combinations, and each combination is repeated 5 times. A total of about 36,000 input combinations.

[0200] 3. Four software tool combinations, namely Racon, canu, Medaka, and Racon+Medaka, are used to calculate a corrected sequence. The corrected sequence is compared with the standard Sanger sequencing result, and the identity value obtained can be considered as the accuracy value of this correction.

[0201] It should be noted that the inventors conducted the above experiments on a variety of bacteria. Since the time results were relatively consistent, in order not to elaborate too much, this example only shows the relevant results of Shigella flexneri.

[0202] The results are as follows Figure 10-12 As shown, it is shown that: 1. The improved Q value of the initial template sequence and the average Q value of the correction library sequence can improve the correction accuracy, which is consistent with the conclusion of Example 1. 2. The Racon+Medaka tool combination has the best correction effect among the four correction tools. 3. Increasing the number of correction library sequences can improve the correction accuracy, but after 40 sequences, the contribution to the correction accuracy is small. 4. When the average Q value of the correction library sequence is 13-14, the Racon+Medaka correction accuracy can be above 99.6%. Considering that the average accuracy of the nanosequencing output sequence is around 13-14, it can be roughly considered that the average accuracy of this correction tool is above 99.6%.

[0203] Example 3: Correction Accuracy Prediction Algorithm

[0204] 1. Based on Example 2, data analysis is performed to construct an algorithm for calculating the accuracy of the calibration sequence by inputting parameters. Two algorithms are compared here.

[0205] 2. Construct multiple linear regression parameters for prediction. The 36,000 data sets in Example 2 were subjected to linear regression analysis using R language, and a linear regression equation was constructed. The accuracy of the calibration sequence can be calculated if the input parameters are known.

[0206] 3. Fitting prediction through existing data. The previous 36,000 sets of data are stored in the program, and the most similar set of input data is selected from the existing data to obtain the most similar correction accuracy value of this set of data.

[0207] 4. The above two prediction accuracy calculation methods are available, and data verification is performed using the standard bacteria data in Example 2. A total of 2,000 groups of different input Q value input combinations are randomly selected, and the prediction accuracy calculations of the two methods are performed respectively.

[0208] Empirical data fitting - average value

[0209] - Overall accuracy: 73%; Error: 0.15

[0210] Multiple Linear Regression - Mean

[0211] - Overall accuracy: 67%; Error: 0.2

[0212] Empirical Data Fitting - Median

[0213] - Overall accuracy: 73%; Error: 0.08

[0214] Multiple Linear Regression - Median

[0215] - Overall accuracy: 63%; Error: 0.17

[0216] Conclusion: 1. The empirical data fitting effect is better. 2. When calculating the prediction accuracy, using the median of the Q value for multiple calibration library sequences is better than using the average of the Q value for prediction.

[0217] Example 4: Real-time calibration process speed and accuracy assessment

[0218] 1. There are two ways to perform calibration analysis on the actual sequencing data. One is to perform calibration after sequencing is completed. The other is the real-time calibration analysis of the present invention.

[0219] 2. The test analysis was conducted on the company's own computing cluster, which has a total of 6 nodes, with a single node configured with a 48-core CPU and 128GB memory. Sequencing was performed on a GridION sequencer, and the sequencing took 6 hours.

[0220] 3. Perform calibration analysis after sequencing is completed. This analysis method completes calibration analysis 74 minutes after sequencing is completed. Only one calibration is performed, and there is no predicted calibration accuracy value.

[0221] 4. The real-time calibration analysis scheme of the present invention completed the calibration analysis 13 minutes after the sequencing was completed. A total of 6 calibration analyses were performed, each with a calibration accuracy value, and the 6 calibration results were finally summarized to obtain the final result.

[0222] 5. After comparative analysis, the correction result obtained by the method of the present invention is more accurate.

[0223] Conclusion: The method of the present invention has a faster running speed for sequence correction and more accurate results.

[0224] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", "some implementation schemes" or "some examples" etc. means 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 specific features, structures, materials or characteristics described 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, unless they are contradictory.

[0225] 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 nanopore sequencing method, characterized in that: The method is used for non-diagnostic purposes, including: (1) Subjecting nucleic acid samples to continuous nanopore sequencing reactions; (2) collecting a first batch of sequencing data at a first time point of the continuous nanopore sequencing reaction, wherein the sequencing data consists of a plurality of sequencing reads; (3) pre-classifying the first batch of sequencing data, obtaining the first batch of sequencing data after pre-classification and aggregating them into a first sequence pool; (4) selecting a first sequencing read combination based on the sequencing quality Q values ​​of the sequencing reads in the first sequence pool; (5) inputting the first sequencing read segment combination into sequencing data correction software to output a first correction result; (6) collecting a second batch of sequencing data at a second time point of the continuous nanopore sequencing reaction, wherein the second batch of sequencing data consists of sequencing data generated between the first time point and the second time point; (7) pre-classifying the second batch of sequencing data to obtain the pre-classified second batch of sequencing data, and aggregating the pre-classified second batch of sequencing data and the data in the first sequence pool into a second sequence pool; (8) selecting a second sequencing read combination based on the sequencing quality Q values ​​of the sequencing reads in the second sequence pool; (9) inputting the second sequencing read combination into sequencing data correction software to output a second correction result; (10) from the start to the end of the nanopore sequencing reaction, including multiple time points, repeating steps (6) to (9) at multiple different time points, counting all calibration results, and ranking the quality of all calibration results, and selecting the highest quality as the final calibration result; The pre-classification is determined by comparing the sequencing data with the reference data, and the first sequencing read combination and the second sequencing read combination are selected according to the sequencing quality Q value from high to low; the first sequencing read combination and the second sequencing read combination each independently include no more than 60 sequencing reads; the median sequencing quality Q value of the sequencing reads of the first sequencing read combination and the second sequencing read combination is not less than 13; the sequencing data correction software includes a combination of Racon and Medaka.

2. The sequencing method according to claim 1, characterized in that The median sequencing quality Q value of the sequencing reads of the first sequencing read combination and the second sequencing read combination is not less than 14.

3. The sequencing method according to claim 1, characterized in that Further comprising predicting the accuracy of the calibration result based on the sequencing quality Q value of the calibration result obtained at each time point selected in the nanopore sequencing reaction, the number of sequencing reads in the sequencing read combination obtained at each time point, and the quality Q value of the sequencing reads in the sequencing read combination obtained at each time point; Optionally, the accuracy is predicted by a machine learning model.

4. The sequencing method according to claim 1, characterized in that Further including: The biological origin of the nucleic acid in the sample is predicted based on the final calibration result.

5. The sequencing method according to claim 1, characterized in that The first time point is no more than 30 minutes from the start of the nanopore sequencing reaction, and the selection of each time point after the first time point meets the following requirements: the interval between each time point and the previous time point is no more than 1 hour until the nanopore sequencing reaction is completed.

6. A nanopore sequencing system, characterized in that: include: A sequencing module, wherein the sequencing module is used to perform continuous nanopore sequencing reactions on nucleic acid samples; a first sequence selection module, the first sequence selection module being used to collect a first batch of sequencing data at a first time point of the continuous nanopore sequencing reaction, pre-classify the first batch of sequencing data, obtain the pre-classified first batch of sequencing data and aggregate them into a first sequence pool, and select a first sequencing read combination based on sequencing quality Q values ​​of sequencing reads in the first sequence pool, wherein the sequencing data consists of a plurality of sequencing reads; A first data correction module, the first data correction module is connected to the first sequence selection module, and the first data correction module is used to input the first sequencing read segment combination into sequencing data correction software to output a first correction result; a second sequence selection module, the second sequence selection module being used to collect a second batch of sequencing data at a second time point of the continuous nanopore sequencing reaction, pre-classify the second batch of sequencing data to obtain the pre-classified second batch of sequencing data, and aggregate the pre-classified second batch of sequencing data and the data in the first sequence pool into a second sequence pool, and select a second sequencing read combination based on the sequencing quality Q value of the sequencing reads in the second sequence pool, wherein the second batch of sequencing data consists of sequencing data generated between the first time point and the second time point; A second data correction module, the second data correction module is used to input the second sequencing read segment combination into sequencing data correction software to output a second correction result; A final calibration result output module, which is used to count all calibration results at multiple different time points from the start to the end of the nanopore sequencing reaction, sort the quality of all calibration results, and select the highest quality as the final calibration result; The pre-classification is determined by comparing the sequencing data with the reference data, and the first sequencing read combination and the second sequencing read combination are selected according to the sequencing quality Q value from high to low; the first sequencing read combination and the second sequencing read combination independently include no more than 60 sequencing reads; the medians of the sequencing quality Q values ​​of the sequencing reads of the first sequencing read combination and the second sequencing read combination are not less than 13; the sequencing data correction software includes a combination of Racon and Medaka.

7. The sequencing system according to claim 6, characterized in that The median sequencing quality Q values ​​of the sequencing reads of the first sequencing read combination and the second sequencing read combination are both not less than 14; Optionally, the system further comprises an accuracy prediction module, the accuracy prediction module being used to predict the accuracy of the calibration result based on the sequencing quality Q value of the calibration result obtained at each time point selected in the nanopore sequencing reaction, the number of sequencing reads in the sequencing read combination obtained at each time point, and the quality Q value of the sequencing reads in the sequencing read combination obtained at each time point; Optionally, the accuracy is predicted by a machine learning model.

8. The sequencing system according to claim 6, characterized in that The system further comprises a prediction module for predicting the biological source of the nucleic acid in the sample based on the final correction result; Optionally, the first time point is no more than 30 minutes from the start of the nanopore sequencing reaction, and each time point after the first time point is selected to meet the following requirements: the interval between each time point and the previous time point is no more than 1 hour until the nanopore sequencing reaction is completed.

9. An electronic device for nanopore sequencing, characterized in that: Including memory and processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the nanopore sequencing method according to any one of claims 1 to 5.

10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the nanopore sequencing method according to any one of claims 1 to 5 is implemented.

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